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Datumbox Machine Learning Framework version 0.8.0 released




Datumbox Framework v0.8.0 is out and packs several powerful features! This version brings new Preprocessing, Feature Selection and Model Selection algorithms, new powerful Storage Engines that give better control on how the Models and the Dataframes are saved/loaded, several pre-trained Machine Learning models and lots of memory & speed improvements. Download it now from Github or Maven Central Repository.

One of the main targets of version 0.8.0 was to improve the Storage mechanisms of the framework and make disk-based training available to all the supported algorithms. The new storage engines give better control over how and when the models are being persisted. One important change is that the models are not being stored automatically after the fit() method is finished but instead one needs to explicitly call the save() method providing the name of the model. This enables us not only to discard easier temporary algorithms without going through a serialization phase but also to save/load the Dataframes:

Configuration configuration = Configuration.getConfiguration();
Dataframe data = ...; //load a dataframe here MaximumEntropy.TrainingParameters params = new MaximumEntropy.TrainingParameters();
MaximumEntropy model = MLBuilder.create(params, getConfiguration());;"MyModel"); //save the model using the specific name
model.close();"MyData"); //save the data using a specific name
data.close(); data = Dataframe.Builder.load("MyData", configuration); //load the data
model = MLBuilder.load(MaximumEntropy.class, "MyModel", configuration); //load the model
model.delete(); //delete the model

Currently we support two storage engines: The InMemory engine which is very fast as it loads everything in memory and the MapDB engine which is slower but permits disk-based training. You can control which engine you use by changing your or you can programmatically modify the configuration objects. Each engine has its own configuration file but again you can modify everything programmatically:

Configuration configuration = Configuration.getConfiguration(); //conf from properties file configuration.setStorageConfiguration(new InMemoryConfiguration()); //use In-Memory engine
//configuration.setStorageConfiguration(new MapDBConfiguration()); //use MapDB engine

Please note that in both engines, there is a directory setting which controls where the models are being stored ( and properties in config files). Make sure you change them or else the models will be written on the temporary folder of your system. For more information on how you structure the configuration files have a look on the Code Example project.

With the new Storage mechanism in place, it is now feasible to share publicly pre-trained models that cover the areas of Sentiment Analysis, Spam Detection, Language Detection, Topic Classification and all the other models that are available via the Datumbox API. You can now download and use all the pre-trained models on your project without requiring calling the API and without being limited by the number of daily calls. Currently the published models are trained using the InMemory storage engine and they support only English. On future releases, I plan to provide support for more languages.

In the new framework, there are several changes on the public methods of many of the classes (hence it is not backwards compatible). The most notable difference is on the way the models are initialized. As we saw in the earlier code example, the models are not directly instantiated but instead the MLBuilder class is used to either create or load a model. The training parameters are provided directly to the builder and they can’t be changed with a setter.

Another improvement is on the way we perform Model Selection. The v0.8.0 introduces the new modelselection package which offers all the necessary tools for validating and measuring the performance of our models. In the metrics subpackage we provide the most important validation metrics for classification, clustering, regression and recommendation. Note that the ValidationMetrics are removed from each individual algorithm and they are no longer stored together with the model. The framework offers the new splitters subpackage which enables splitting the original dataset using different schemes. Currently K-fold splits are performed using the KFoldSplitter class while partitioning the dataset into a training and test set can be achieved with the ShuffleSplitter. Finally to quickly validate a model, the framework offers the Validator class. Here is how one can perform K-fold cross validation within a couple of lines of code:

ClassificationMetrics vm = new Validator<>(ClassificationMetrics.class, configuration) .validate(new KFoldSplitter(k).split(data), new MaximumEntropy.TrainingParameters());

The new Preprocessing package replaces the old Data Transformers and gives better control on how we scale and encode the data before the machine learning algorithms. The following algorithms are supported for scaling numerical variables: MinMaxScaler, StandardScaler, MaxAbsScaler and BinaryScaler. For encoding categorical variables into booleans you can use the following methods: OneHotEncoder and CornerConstraintsEncoder. Here is how you can use the new algorithms:

StandardScaler numericalScaler = MLBuilder.create( new StandardScaler.TrainingParameters(), configuration
numericalScaler.fit_transform(trainingData); CornerConstraintsEncoder categoricalEncoder = MLBuilder.create( new CornerConstraintsEncoder.TrainingParameters(), configuration

Another important update is the fact that the Feature Selection package was rewritten. Currently all feature selection algorithms focus on specific datatypes, making it possible to chain different methods together. As a result the TextClassifier and the Modeler classes receive a list of feature selector parameters rather than just one.

As mentioned earlier all the algorithms now support disk-based training, including those that use Matrices (only exception is the Support Vector Machines). The new storage engine mechanism even makes it possible to configure some algorithms or dataframes to be stored in memory while others on disk. Several speed improvements were introduced primarily due to the new storage engine mechanism but also due to the tuning of individual algorithms such as the ones in the DPMM family.

Last but not least the new version updates all the dependencies to their latest versions and removes some of them such as the the commons-lang and lp_solve. The commons-lang, which was used for HTML parsing, is replaced with a faster custom HTMLParser implementation. The lp_solve is replaced with a pure Java simplex solver which means that Datumbox no longer requires specific system libraries installed on the operating system. Moreover lp_solve had to go because it uses LGPLv2 which is not compatible with the Apache 2.0 license.

The version 0.8.0 brings several more new features and improvements on the framework. For a detailed view of the changes please check the Changelog.

Don’t forget to clone the code of Datumbox Framework v0.8.0 from Github, check out the Code Examples and download the pre-trained Machine Learning models from Datumbox Zoo. I am looking forward to your comments and suggestions.

About Vasilis Vryniotis

My name is Vasilis Vryniotis. I’m a Data Scientist, a Software Engineer, author of Datumbox Machine Learning Framework and a proud geek. Learn more



10 Ways Machine Learning Practitioners Can Build Fairer Systems




Author profile picture

@skylerwhartonSkyler Wharton

Software Engineer (ML & Backend) @ Airbnb. My opinions are my own. [they/them]

An introduction to the harm that ML systems cause and to the power imbalance that exists between ML system developers and ML system participants …and 10 concrete ways for machine learning practitioners to help build fairer ML systems.

Image caption: Photo by Koshu Kunii on Unsplash. Image description: Photo of Black Lives Matter protesters in Washington, D.C. — 2 signs say “Black Lives Matter” and “White Silence is Violence.”

Machine learning systems are increasingly used as tools of oppression. All too often, they’re used in high-stakes processes without participants’ consent and with no reasonable opportunity for participants to contest the system’s decisions — like when risk assessment systems are used by child welfare services to identify at-risk children; when a machine learning (or “ML”) model decides who sees which online ads for employment, housing, or credit opportunities; or when facial recognition systems are used to surveil neighborhoods where Black and Brown people live.

ML systems are deployed widely because they are viewed as “neutral” and “objective.”

In reality though, machine learning systems reflect the beliefs and biases of those who design and develop them.

As a result, ML systems mirror and amplify the beliefs and biases of their designers, and are at least as susceptible to making mistakes as human arbiters.

When ML systems are deployed at scale, they cause harm — especially when their decisions are wrong. This harm is disproportionately felt by members of marginalized communities [1]. This is especially evident in this moment, when people protesting as part of the global movement for Black Lives are being tracked by police departments using facial recognition systems [2] and when an ML system was recently used to determine students’ A-level grades in the U.K. after the tests were cancelled due to the pandemic, jeopardizing the futures of poorer students, many of whom are people of color and immigrants [3].

In this post, I’ll describe some examples of harm caused by machine learning systems. Then I’ll offer some concrete recommendations and resources that machine learning practitioners can use to develop fairer machine learning systems. I hope this post encourages other machine learning practitioners to start using and educating their peers about practices for developing fairer ML systems within their teams and companies.

How machine learning systems cause harm

In June 2020, Robert Williams, a Black man, was arrested by the Detroit Police Department because a facial recognition system identified him as the person who committed a recent shoplifting; however, visual comparison of his face to the face in the photo clearly revealed that they weren’t the same person [4].

Nevertheless, Mr. Williams was arrested, interrogated, kept in custody for more than 24 hours, released on bail on his own money, and had to court before his case was dismissed.

This “accident” significantly harmed Mr. Williams and his family:

  • He felt humiliated and embarrassed. When interviewed by the New York Times about this incident, he said, “My mother doesn’t know about it. It’s not something I’m proud of … It’s humiliating.”
  • It caused lasting trauma to him and his family. Had Mr. Williams resisted arrest — which would have been reasonable given that it was unjust — he could have been killed. As it was, the experience was harrowing. He and his wife now wonder whether they need to put their two young daughters into therapy.
  • It put his job — and thus his ability to support himself and his family — at risk. He could have lost his job, even though his case was ultimately dismissed; companies have fired employees with impunity for far less. Fortunately, his boss was understanding of the situation, but his boss still advised him not to tell others at work.
  • It nearly resulted in him having a permanent criminal record. When Mr. Williams went to court, his case was initially dismissed “without prejudice,” which meant that he could still be charged later. Only after the false positive received widespread media attention did the prosecutor apologize and offer to expunge his record and fingerprints.

The harms caused here by a facial recognition system used by a local police department are unacceptable.

Facebook’s ad delivery system is another example of a harmful machine learning system. In 2019, Dr. Piotr Sapieżyński, a research scientist at Northeastern University, and his collaborators conducted an experiment using Facebook’s own marketing tools to discover how employment ads are distributed on Facebook [5, 6]. Through this experiment, they discovered that Facebook’s ad delivery system, despite neutral targeting preferences, shows significantly different job ads to each user depending upon their gender and race. In other words, even if an advertiser specifies that they want their ad to be seen uniformly by all genders and all races, Facebook’s ad delivery system will, depending on the content of the ad, show the ad to a race- and/or gender-skewed audience.

Specifically, Dr. Sapieżyński and collaborators discovered that women are more likely to receive ads for supermarket, janitor, and preschool jobs, whereas men are more likely to receive ads for taxi, artificial intelligence, and lumber jobs. (The researchers acknowledge that the study was limited to binary genders due to restrictions in Facebook’s advertising tools.) They similarly discovered that Black people are more likely to receive ads for taxi, janitor, and restaurant jobs, whereas white people are more likely to receive ads for secretary, artificial intelligence, and lumber jobs.

Facebook’s ad delivery system is an example of a consumer-facing ML system that causes harm to those who participate in it:

  • It perpetuates and amplifies gender- and race-based employment stereotypes for people who use Facebook. For example, women are shown ads for jobs that have historically been associated with “womanhood” (e.g., caregiving or cleaning jobs); seeing such ads reinforces their own — and also other genders’ — perceptions of jobs that women can or “should” do. This is also the case for the ads shown to Black people.
  • It restricts Black users’ and woman users’ access to economic opportunity. The advertisements that Facebook shows to Black people and women are for noticeably lower-paying jobs. If Black people and women do not even know about available higher-paying jobs, then they are unable to apply for and be hired for them.

The harms caused by Facebook’s ad delivery system are also unacceptable.

Broader context

In the case of both aforementioned algorithmic systems, the harm they cause goes deeper: they amplify existing systems of oppression, often in the name of “neutrality” and “objectivity.” In other words, the examples above are not isolated incidents; they contribute to long-standing patterns of harm.

For example, Black people, especially Black men and Black masculine people, have been systematically overpoliced, targeted, and murdered for the last four hundred years. This is undoubtedly still true, as evidenced by the recent murders by the police of George Floyd, Breonna Taylor, Tony McDade, and Ahmaud Arbery and recent shooting by the police of Jacob Blake.

Commercial facial recognition systems allow police departments to more easily and subtly target Black men and masculine people, including to target them at scale. A facial recognition system can identify more “criminals” in an hour than a hundred police officers could in a month, and it can do so less expensively. Thus, commercial facial recognition systems allow police departments to “mass produce” their practice of overpolicing, targeting, and murdering Black people.

Moreover, in 2018, computer science researchers Joy Buolamwini and Dr. Timnit Gebru showed that commercial facial recognition systems are significantly less accurate for darker-skinned people than they are for lighter-skinned people [7]. Indeed, when used for surveillance, facial recognition systems identify the wrong person up to 98% of the time [8]. As a result, when allowed to be used by police departments, commercial facial recognition systems cause harm not only by “scaling” police forces’ discriminatory practices but also by identifying the wrong person the majority of the time.

Facebook’s ad delivery system also amplifies a well-documented system of oppression: wealth inequality by race. In the United States, the median adjusted household income of white and Asian households is 1.6x greater than that of Black and Hispanic households (~$71K vs. $43K), and the median net worth of white households is 13x greater than that of Black households (~$144K vs. $11K) [9]. Thus, by consistently showing ads for only lower-paying jobs to the millions of Black people who use Facebook, Facebook is entrenching and widening the wealth gap between Black people and more affluent demographic groups (especially white people) in the United States. Facebook’s ad delivery system is likely similarly amplifying wealth inequities in other countries around the world.

How collecting labels for machine learning systems causes harm

Harm is not only caused by machine learning systems that have been deployed; harm is also caused while machine learning systems are being developed. That is, harm is often caused while labels are being collected for the purpose of training machine learning models.

For example, in February 2019, The Verge’s Casey Newton released a piece about the working conditions inside Cognizant, a vendor that Facebook hires to label and moderate Facebook content [10]. His findings were shocking: Facebook was essentially running a digital sweatshop.

What they discovered:

  • Employees were underpaid: In Phoenix, AZ, a moderator made $28,800/year (versus the $240,000/year total compensation of a full-time Facebook employee).
  • Working conditions at Cognizant were abysmal: Employees were often fired after making just a few mistakes a week. Since a “mistake” occurred when two employees disagreed about how a piece of content should be moderated, resentment grew between employees. Fired employees often threatened to return to work and harm their old colleagues. Additionally, employees were micromanaged: they got two 15-minute breaks and one 30-minute lunch per day. Much of their break time was spent waiting in line for the bathroom, as often >500 people had to share six bathroom stalls.
  • Employees’ mental health was damaged: Moderators spent most of their time reviewing graphically violent or hateful content, including animal abuse, child abuse, and murders. As a result of watching six hours per day of violent or hateful content, employees developed severe anxiety, often while still in training. After leaving the company, employees developed symptoms of PTSD. While employed, employees had access to only nine minutes of mental health support per day; after they left the company, they had no mental health support from Facebook or Cognizant.

Similar harms are caused by crowdsourcing platforms like Amazon Mechanical Turk, through which individuals, academic labs, or companies submit tasks for “crowdworkers” to complete:

  • Employees are underpaid. Mechanical Turk and other similar platforms are premised on a large amount of unpaid labor: workers are not paid to find tasks, for tasks they start but can’t complete due to vague instructions, for tasks rejected by task authors for often arbitrary reasons, or for breaks. As a result, the median wage for a crowdworker on Mechanical Turk is approximately $2/hour [11]. Workers who do not live in the United States, are women, and/or are disabled are likely to earn much less per hour [12].
  • Working conditions are abysmal. Workers’ income fluctuates over time, so they can’t plan for themselves or their families for the long-term; workers don’t get healthcare or any other benefits; and workers have no legal protections.
  • Employees’ mental health is damaged. Crowdworkers often struggle to find enough well-paying tasks, which causes stress and anxiety. For example, workers report waking up at 2 or 3am in order to get tasks that pay better [11].

Contrary to popular belief, many people who complete tasks on crowdsourcing platforms do so in order to earn the bulk of their income. Thus, people who work for private labeling companies like Cognizant and people who work for crowdsourcing platforms like Mechanical Turk have a similar goal: to complete labeling tasks in a safe and healthy work environment in exchange for fair wages.

Why these harms are happening

At this point, you might be asking yourself, “Why are these harms happening?” The answer is multifaceted: there are many reasons why deployed machine learning systems cause harm to their participants.

When ML systems are used

A big reason that machine learning systems cause harm is due to the contexts in which they’re used. That is, because machine learning systems are considered “neutral” and “objective,” they’re often used in high-stakes decision processes as a way to save money. High-stakes decision processes are inherently more likely to cause harm, since a mistake made during the decision process could have a significant negative impact on someone’s life.

At best, introducing a machine learning system into a high-stakes decision process does not affect the probability that the system causes harm; at worst, it increases the probability of harm, due to machine learning models’ tendency to amplify biases against marginalized groups, human complacency around auditing the model’s decisions (since they’re “neutral” and “objective”), and that machine learning models’ decisions are often uninterpretable.

How ML systems are designed

Machine learning systems also cause harm because of how they’re designed. For example, when designing a system, engineers often do not account for the possibility that the system could make an incorrect decision; thus, machine learning systems often do not include a mechanism for participants to feasibly contest the decision or seek recourse.

Whose perspectives are centered when ML systems are designed

Another reason that ML systems cause harm is that the perspectives of people who are most likely to be harmed by them are not centered when the system is being designed.

Systems designed by people will reflect the beliefs and biases — both conscious and unconscious — of those people. Machine learning systems are overwhelmingly built by a very homogenous group of people: white, Asian-American, or Asian heterosexual cisgender men who are between 20 and 50 years old, who are able-bodied and neurotypical, who are American and/or who live in the United States, and who have a traditional educational background, including a degree in computer science from one of ~50 elite universities. As a result, machine learning systems are biased towards the experiences of this narrow group of people.

Additionally, machine learning systems are used in often contexts that disproportionately involve historically marginalized groups (like predicting recidivism or surveilling “high crime” neighborhoods) or to determine access to resources that have long been unfairly denied to marginalized groups (like housing, employment opportunities, credit and loans, and healthcare). For example, since Black people have historically been denied fair access to healthcare, machine learning systems used in such contexts display similar patterns of discrimination, because they hinge on historical assumptions and data [13]. As a result, unless deliberate action is taken to center the experiences of the groups that ML systems are arbitrating, machine learning systems lead to history repeating itself.

At the intersection of the aforementioned two points is a chilling realization: the people who design machine learning systems are rarely the people who are affected by machine learning systems. This rings eerily similar to the fact that most police do not live in the cities where they work [14].

Lack of transparency around when ML systems are used

Harm is also caused by machine learning systems because it’s often unclear when an algorithm has been used to make a decision. This is because companies are not required to disclose when and how machine learning systems are used (much less get participants’ consent), even when the outcomes of those decisions affect human lives. If someone is unaware that they’ve been affected by an ML system, then they can’t attribute harm they may have experienced to it.

Additionally, even if a person knows or suspects that they’ve been harmed by a machine learning system, proving that they’ve been discriminated against is difficult or impossible, since the complete set of decisions made by the ML system is private and thus cannot be audited for discrimination. As a result, harm that machine learning systems cause often cannot be “proven.”

Lack of legal protection for ML system participants

Finally, machine learning systems cause harm because there is currently very little regulatory or legal oversight around when and how machine learning systems are used, so companies, governments, and other organizations can use them to discriminate against participants with impunity.

With respect to facial recognition, this is slowly changing: in 2019, San Francisco became the first major city to ban the use of facial recognition by local government agencies [15]. Since then, several other cities have done the same, including Oakland, CA; Somerville, MA; and Boston, MA [16, 17].

Nevertheless, there are still hundreds of known instances of local government agencies using facial recognition, including at points of entry into the United States like borders and airports and by local police for unspecified purposes [18]. Use of facial recognition systems in these contexts — especially given that the majority of their decisions are likely wrong [8] — have real-world impact, including harassment, unjustified imprisonment, and deportation.

With respect to other types of machine learning systems, there have been few legal advances.

Call to action

Given the contexts in which ML systems are used, the current lack of legal and regulatory oversight for such contexts, and the lack of societal power that people harmed by ML systems tend to have (due to their, e.g., race, gender, disability, citizenship, and/or wealth), ML system developers have massively more power than participants.

Image caption: There are huge power imbalances in machine learning system development: ML system developers have more power than ML system participants, and labeling task requesters have more power than labeling agents. [Image source:] Image description: Imbalanced scale image — ML system developer & labeling task requester weigh more than ML system participant & labeling agent

There’s a similar power dynamic between people who design labeling tasks and people who complete labeling tasks: labeling task requesters have more power than labeling agents.

Here, ML system developer is defined as anyone who is involved in the design, development, and deployment of machine learning systems, including machine learning engineers and data scientists and also software engineers of other technical disciplines, product managers, engineering managers, UX researchers, UX writers, lawyers, mid-level managers, and C-suite executives. All of these roles are included in order to emphasize that even if you don’t work directly on a machine learning system, if you work at a company or organization that uses machine learning systems, then you have power to affect change on when and how machine learning is used at your company.

Let me be clear: individual action is not enough — we desperately need well-designed legislation to guide when and how ML systems can be used. Importantly, there should be some contexts in which ML systems cannot be used, no matter how “accurate” they are, because the probability of misuse and mistakes are too great — like police departments using facial recognition systems [19].

Unfortunately, we do not have necessary legislation and regulation in place yet. In the meantime, as ML system developers, we should intentionally consider the ML systems that we, our teams, or our companies own and utilize.

How to build fairer machine learning systems

If you are a machine learning system developer — especially if you are machine learning practitioner, like an ML engineer or data scientist — here are 10 ways you can help build machine learning systems that are more fair:


When designing a new ML system or evaluating an existing ML system, ask yourself and your team the following questions about the context in which the system is being deployed/is deployed [20]:

  • What could go wrong when this ML system is deployed?
  • When something goes wrong, who is harmed?
  • How likely is it that something will go wrong?
  • Does the harm disproportionately fall on marginalized groups?

Use your answers to these questions to evaluate how to proceed. For example, if possible, proactively engineer solutions that prevent harms from occurring (e.g., add safeguards to prevent harm, like including human intervention and mechanisms for participants to contest system decisions, and inform participants that a machine learning algorithm is being used). Alternately, if the likelihood and scale of harm are too high, do not deploy it. Instead, consider pursuing a solution that does not depend on machine learning or that uses machine learning in a less risky way. Deploying a biased machine learning system can cause real-world harm to system participants as well as reputational damage to your company [21, 22, 23].


Utilize best practices for developing fairer ML systems. Machine learning fairness researchers have been designing and testing best practices for several years now. For example, one best practice is to, when releasing a dataset for public or internal use, simultaneously release a datasheet, a short document that shares information that consumers of the dataset need in order to make informed decisions about using it (e.g., mechanisms or procedures used to collect the data, whether an ethical review process was conducted, whether or not the dataset relates to people) [24].

Similarly, when releasing a trained model for public or internal use, simultaneously release a model card, a short document that shares information about the model (e.g., evaluation results (ideally disaggregated across different demographic groups and communities), intended usage(s), usages to avoid, insight into model training processes) [25].

Finally, consider implementing a company-wide process for internal algorithmic auditing, like that which Deb RajiAndrew Smart, and their collaborators proposed in their 2020 paper Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing.


Work with your company or organization to develop partnerships with advocacy organizations that represent groups of people that machine learning systems tend to marginalize, in order to responsibly engage marginalized communities as stakeholders. Examples of such organizations include Color Of Change and the NAACP. Then, while developing new machine learning systems or evaluating existing machine learning systems, seek and incorporate their feedback.


Hire machine learning engineers and data scientists from underrepresented backgrounds, especially Black people, Indigenous people, Latinx people, disabled people, transgender and nonbinary people, formerly incarcerated people, and people from countries that are underrepresented in technology (e.g., countries in Africa, countries in Southeast Asia, and counties in South America). Note that this will require rethinking how talent is discovered and trained [26] — consider recruiting from historically-black colleges and universities (HBCUs) in the U.S. and coding and data science bootcamps or starting an internal program like Slack’s Next Chapter.

On a related note, work with your company to support organizations that foster talent from underrepresented backgrounds, like AI4ALLBlack Girls CodeCode2040NCWITTECHNOLOchicas, TransTech, and Out for Undergrad. Organizations like these are critical for increasing the number of people from underrepresented backgrounds in technology jobs, including in ML/AI jobs, and all of them have a proven track record of success. Additionally, consider supporting organizations like these with your own money and time.


Work with your company or organization to sign the Safe Face Pledge, an opportunity for organizations to make public commitments towards mitigating the abuse of facial analysis technology. This pledge was jointly drafted by the Algorithmic Justice League and the Center on Technology & Privacy at Georgetown Law, and has already been signed by many leading ethics and privacy experts.


Learn more about the ways in which machine learning systems cause harm. For example, here are seven recommended resources to continue learning:

  1. [Book] Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil (2016)
  2. [Book] Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Noble (2018)
  3. [Book] Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard (2018)
  4. [Book] Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor by Virginia Eubanks (2019)
  5. [Book] Race After Technology: Abolitionist Tools for the New Jim Code by Ruha Benjamin (2019)
  6. [Book] Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass by Mary L. Gray and Siddharth Suri (2019)
  7. [Film] Coded Bias (2020)

Additionally, you can learn more about harms caused by ML systems by reading the work of journalists and researchers who are uncovering biases in machine learning systems. In addition to the researchers and journalists I’ve already named in this essay (e.g., Dr. Piotr SapieżyńskiCasey Newton, Joy BuolamwiniDr. Timnit GebruDeb RajiAndrew Smart), some examples include Julia Angwin (and anything written by The Markup), Khari JohnsonMoira WeigelLauren Kirchner, and anything written by Upturn. The work of journalists and researchers serve as important case studies for how not to design machine learning systems, which is valuable for ML practitioners’ who are aiming to develop fair and equitable ML systems.


Learn about ways in which existing machine learning systems have been improved in order to cause less harm. For example, IBM has worked to improve the performance of their commercial facial recognition system with respect to racial and gender bias (direct link), Google has worked to reduce gender bias in Google Translate (direct link), and Jigsaw (within Google) has worked to change Perspective AI (a public API for hate speech detection algorithm) to less often classify phrases containing frequently targeted groups (e.g., Muslims, women, queer people) as being hate speech (direct link).


Conduct an audit of a machine learning system for disparate impact. Disparate impact occurs when, even though a policy or system is neutral, one group of people is adversely affected more than another. Facebook’s ad delivery system is an example of a system causing disparate impact.

For example, use Project Lighthouse, a methodology that Airbnb released earlier this year that uses anonymized demographic data to measure user experience discrepancies that may be due to discrimination or bias, or ArthurAI, an ML monitoring framework that allows you to also monitor model bias. (Full disclosure: I work at Airbnb.)

Alternatively, hire an algorithmic consulting firm to conduct an audit of a machine learning system that your team or company owns, like O’Neil Risk Consulting & Algorithmic Auditing or the Algorithmic Justice League.


When hiring third-party vendors or using crowdsourcing platforms for machine learning labeling tasks, be critical of who you choose to support. Inquire about the working conditions of the people who will be labeling for you. Additionally, if possible, make an onsite visit to the vendor to gauge working conditions for yourself. What is their hourly pay? Do they have healthcare and other benefits? Are they full-time employees or contractors? Do they expose their workforce to graphically violent or hateful content? Are there opportunities for career growth and advancement within the company?


Give a presentation to your team or company about harms that machine learning systems’ cause and how to mitigate them. The more people who understand the harms that machine learning systems cause and the power imbalance that currently exists between ML system developers and ML system participants, the more likely it is that we can affect change on our teams and in our companies.


Finally, the bonus #11 in this list is, if you are eligible to do so in the United States, VOTE. There is so much at stake in this upcoming election, including the rights of BIPOC people, immigrants, women, LGBTQ people, and disabled people as well as — quite literally — the future of our democracy. If you are not registered to vote, please do so now: Register to vote. If you are registered to vote but have not requested your absentee or mail-in ballot, please do so now: Request your absentee ballotEven though Joe Biden is far from the perfect candidate, we need to elect him and Kamala Harris; this country, the people in it, and so many people around the world cannot survive another four years of a Trump presidency.


Machine learning systems are incredibly powerful tools; unfortunately though, they can be either agents of empowerment or agents of harm. As machine learning practitioners, we have a responsibility to recognize the harm that systems we build cause and then act accordingly. Together, we can work toward a world in which machine learning systems are used responsibly, do not reinforce existing systemic biases, and uplift and empower people from marginalized communities.

This piece was inspired in part by Participatory Approaches to Machine Learning, a workshop at the 2020 International Conference on Machine Learning (ICML) that I had the opportunity to attend in July. I would like to deeply thank the organizers of this event for calling attention to the power imbalance between ML system developers and ML system participants and for creating a space to discuss it: Angela ZhouDavid MadrasInioluwa Deborah RajiBogdan KulynychSmitha Milli, and Richard Zemel. Also published at here.


[1] Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil. Published 2016.

[2] NYPD used facial recognition to track down Black Lives Matter activistThe Verge. August 18, 2020.

[3] An Algorithm Determined UK Students’ Grades. Chaos EnsuedWired. August 15, 2020.

[4] Wrongfully Accused by an AlgorithmThe New York Times. June 24, 2020.

[5] Discrimination through Optimization: How Facebook’s Ad Delivery Can Lead to Biased Outcomes. Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. CSCW 2019.

[6] Turning the tables on Facebook: How we audit Facebook using their own marketing tools. Piotr Sapiezynski, Muhammad Ali, Aleksandra Korolova, Alan Mislove, Aaron Rieke, Miranda Bogen, and Avijit Ghosh. Talk given at PAML Workshop at ICML 2020.

[7] Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Joy Buolamwini and Timnit Gebru. ACM FAT* 2018.

[8] Facial-recognition software inaccurate in 98% of cases, report findsCNET. May 13, 2018.

[9] On Views of Race and Inequality, Blacks and Whites Are Worlds Apart: Demographic trends and economic well-beingPew Research Center. June 27, 2016.

[10] The Trauma Floor: The secret lives of Facebook moderators in AmericaThe Verge. February 25, 2019.

[11] The Internet Is Enabling a New Kind of Poorly Paid HellThe Atlantic. January 23, 2018.

[12] Worker Demographics and Earnings on Amazon Mechanical Turk: An Exploratory Analysis. Kotaro Hara, Abigail Adams, Kristy Milland, Saiph Savage, Benjamin V. Hanrahan, Jeffrey P. Bigham, and Chris Callison-Burch. CHI Late Breaking Work 2019.

[13] Millions of black people affected by racial bias in health-care algorithmsNature. October 24, 2019.

[14] Most Police Don’t Live In The Cities They ServeFiveThirtyEight. August 20, 2014.

[15] San Francisco’s facial recognition technology ban, explainedVox. May 14, 2019.

[16] Beyond San Francisco, more cities are saying no to facial recognitionCNN. July 17, 2019.

[17] Boston is second-largest US city to ban facial recognitionSmart Cities Dive. July 6, 2020.

[18] Ban Facial Recognition: Map. Accessed August 30, 2020.

[19] Defending Black Lives Means Banning Facial RecognitionWired. July 10, 2020.

[20] Credit for the framing goes to Dr. Cathy O’Neil, of O’Neil Risk Consulting & Algorithmic Auditing.

[21] Amazon reportedly scraps internal AI recruiting tool that was biased against womenThe Verge. October 10, 2018.

[22] Google ‘fixed’ its racist algorithm by removing gorillas from its image-labeling techThe Verge. January 12, 2018.

[23] Facebook’s ad-serving algorithm discriminates by gender and raceMIT Technology Review. April 5, 2019.

[24] Datasheets for Datasets. Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. ArXiv preprint 2018.

[25] Model Cards for Model Reporting. Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. ACM FAT* 2019.

[26] Combating Anti-Blackness in the AI Community.

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Singapore Organizations Adopt AI, ML Amid COVID-19 Induced Uncertainties




Amid the COVID-19 pandemic, Singapore businesses are turning to artificial intelligence (AI) and machine learning (ML) to manage consumer credit risk and deal with economic uncertainties, according to a new research by information services company Experian.

Experian, which surveyed 3,000 consumers and 900 executives working in retail banking, e-commerce, consumer technology and telecommunications, found that COVID-19 has accelerated adoption of digital solutions.

Singapore organizations in particular are embracing AI and ML at a much faster pace than their international peers, with 78% of organizations already using AI to cope with today’s marketplace unpredictability while 79% are leveraging ML. These are higher than the global figure of 69%.

S&P Global Ratings estimates that Asia Pacific (APAC) financial institutions will be hit with US$1.4 trillion in additional nonperforming assets and additional credit costs of about US$440 million as risks associated with COVID-19 and market volatility take hold.

Against this backdrop, 25% of Singapore-based respondents are planning to use on-demand cloud-based decisioning applications, policy rules (25%) and automated decision management (24%) to help them effectively determine which consumers can be safely given extended credit. Over the next 12 months, 69% will be allocating resources towards building their analytics capabilities to assess customer creditworthiness, the survey found.

Online shopping and e-commerce on the rise

Singaporean businesses’ willingness to invest in and adopt digital solutions comes at a time when consumers are demanding better digital-first experiences. A research conducted in June by market research consultancy Blackbox and survey firm Toluna found that while consumers spent more online during the pandemic, about four in ten Singaporeans said they were not satisfied with their e-commerce experience, noting that delivery costs, product prices and delivery time could be better improved.

That being said, global marketing research firm Nielsen expects the penetration of users venturing into e-commerce to continue to rise. Nielsen’s COVID-19 dipstick in March 2020 found that 69% of Singaporean people surveyed who bought household goods online for the first time during COVID-19 will do so again in the next 12 months.

Similarly, Standard Chartered, which polled 12,000 consumers across 12 markets in August 2020, found that, amid COVID-19, Singaporean consumers that prefer online purchases to in-person card or cash payments increased to 50%, up from 35% before the pandemic.

Changing spending habits

Globally, the COVID-19 crisis and its ramifications have disrupted markets and deteriorated the health and economic welfare of consumers. In Singapore, 23% of respondents still face challenges in paying credit card bills, while 20% are encountering difficulties paying their utility bills, the Experian research found. This has prompted many consumers to rethink their spending habits, shifting to essentials and cutting back on most discretionary categories.

In Singapore, consumers are taking steps to manage these financial challenges by reducing their expenditure on non-essentials (22%), saving more (22%), and starting a personal budget (17%), the study found.

According to the Standard Chartered survey, consumers in the city-state are spending about 15-52% more on groceries, digital devices and healthcare, but spend less on clothes, experiences and travel or holidays.

Almost eight in ten respondents in Singapore said they would like to be better at managing their finances, and six in ten said the pandemic has made them more likely to track their spending. Most of the respondents are either user or interested in using budgeting as well as finance tracking tools.

Jeremy Soo, head of consumer banking at DBS Bank, told Fintech News Singapore in September, that, amid COVID-19, people were starting financial planning earlier. Since the bank launched its new digital financial planning tool, NAV Planner, back in April, over one million customers had used it, Soo said.

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How to Get the Best Start at Sports Betting




If you are looking into getting into sports betting, then you might be hesitant about how to start, and the whole idea of it can be quite daunting. There are many techniques to get the best possible start at sports betting and, in this article, we will have a look at some of the best tips for that.

Mental preparation

This sounds a bit pretentious, but it is very important to understand some things about betting before starting so you can not only avoid nasty surprises but also avoid losing too much money. Firstly, you need to know that, in the beginning, you will not be good at betting. It is through experience and learning from your mistakes that you will get better. It is imperative that you do not convince yourself that you are good at betting, especially if you win some early bets, because I can guarantee it will have been luck – and false confidence is not your friend. 

It is likely that you will lose some money at first, but this is to be expected. Almost any hobby that you are interested in will cost you some money so, instead, look at it as an investment. However, do not invest ridiculous amounts; rather, wait until you are confident in your betting ability to start placing larger stakes. 

Set up different accounts

This is the best way to start with sports betting, as the welcome offers will offset a lot of the risk. These offers are designed to be profitable to entice you into betting with the bookie, but it is completely legal to just profit from the welcome offer and not bet with the bookie again. 

If you do this with the most bookies, as you can, you are minimising the risk involved with your betting and maximising possible returns, so it really is a no-brainer.

As well as this clear advantage, different betting companies offer different promotions. Ladbrokes offer a boost every day, for example, where you can choose your bet and boost it a little bit, and the Parimatch betting website chooses a bet for big events and doubles the odds. 

If you are making sure you stay aware of the best offers across these platforms, then you will be able to use the most lucrative ones and, as such, you will be giving yourself the best chance of making money. The house always wins, as they say, but if you use this tip, you are skewing the odds back in your favour. 

Remember, the house wins because of gamblers that do not put in the effort and do not bet smart. Avoid those mistakes and you will massively increase your chances of making money.


On Twitter, especially, but also other social media platforms, there are tipsters who offer their bets for free. It is not so much the bets themselves that you are interested in, but rather why they are betting on this. It is important that you find tipsters who know what they are doing, though, because there are a lot of tipsters who are essentially scamming their customers. It is quite easy to find legitimate tipsters because they are not afraid to show their mistakes. 

Once you have found good tipsters, then you need to understand the reasoning behind their bets. When you have done that, you can start placing these bets yourself, and they will likely be of better value since some tipsters influence the betting markets considerably. You can also follow their bets as they are likely to be sensible bets, although this does not necessarily translate to success.


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