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AI Ethics Guidelines from Diverse Groups: The Consensus?

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We are yet to see a holistic framework for the ethical development of artificial intelligence applications that can be applied to every industry in every country around the world. A lot of work is being done by corporate entities as well as academia, not to mention special-interest groups that warn of the dangers of uncontrolled AI proliferation. Nevertheless, we’re still a long way from a consensus on what it should involve.

This snapshot of the views of various entities with regard to AI principles could offer a clue to what is really missing in our quest for AI governance of the future.

Google

Despite the company disbanding its Advanced Technology External Advisory Council (ATEAC) after only one week due to internal controversy, Google already has the recipe for proper AI research. As listed in their blog, here are the key points:

“We will assess AI applications in view of the following objectives. We believe that AI should:

  • Be socially beneficial.
  • Avoid creating or reinforcing unfair bias.
  • Be built and tested for safety.
  • Be accountable to people.
  • Incorporate privacy design principles.
  • Uphold high standards of scientific excellence.
  • Be made available for uses that accord with these principles.”

While there are a lot of good points in there, a lot of questions crawl out of the woodwork when you talk about weaponizing AI. For example, where is their stand against using AI in warfare or other harmful acts like cyber attacks? As part of that last point above, the blog does say that they will evaluate the “primary purpose and use” and see if it is “related to or adaptable to a harmful use,” but little more than that.

Microsoft

Microsoft has a slightly different set of beliefs, some of which align with Google’s, but only in the broader sense. Again, there’s nothing that directly mentions ways to tackle the AI weaponization issue.

“Designing AI to be trustworthy requires creating solutions that reflect ethical principles that are deeply rooted in important and timeless values.

  • Fairness: AI systems should treat all people fairly
  • Inclusiveness: AI systems should empower everyone and engage people
  • Reliability & Safety: AI systems should perform reliably and safely
  • Transparency: AI systems should be understandable
  • Privacy & Security: AI systems should be secure and respect privacy
  • Accountability: AI systems should have algorithmic accountability”

To be fair, neither company has the ability to control what happens at the international level, so it’s understandable that their AI tenets are limited to positive applications. Not condonable, but understandable. So let’s see where the European Union stands on AI principles.

European Union

The EU’s stance is a lot more inclusive in the press release it issued earlier this month, and it does account for various aspects including how AI can and cannot be applied.

“AI should respect all applicable laws and regulations, as well as a series of requirements; specific assessment lists aim to help verify the application of each of the key requirements:

  • Human agency and oversight: AI systems should enable equitable societies by supporting human agency and fundamental rights, and not decrease, limit or misguide human autonomy.
  • Robustness and safety: Trustworthy AI requires algorithms to be secure, reliable and robust enough to deal with errors or inconsistencies during all life cycle phases of AI systems.
  • Privacy and data governance: Citizens should have full control over their own data, while data concerning them will not be used to harm or discriminate against them.
  • Transparency: The traceability of AI systems should be ensured.
  • Diversity, non-discrimination and fairness: AI systems should consider the whole range of human abilities, skills and requirements, and ensure accessibility.
  • Societal and environmental well-being: AI systems should be used to enhance positive social change and enhance sustainability and ecological responsibility.
  • Accountability: Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes.”

This looks a lot closer to what we all want to see, and the very first item covers the misuse of AI, albeit in a very generic way. However, it does bring up “societal and environmental well-being”, which is clearly an allusion to not using AI to disrupt social and environmental balances. It looks like the EU has mulled over this for a longer time than Google or Microsoft.

But it’s the Future of Life Institute that clearly outlines and addresses the dangers of uncontrolled AI development.

Future of Life Institute’s Asilomar Principles

These guidelines have been in place for the past two years, and so far offer the only viable base for a framework of any sort.

“Artificial intelligence has already provided beneficial tools that are used every day by people around the world. Its continued development, guided by the following principles, will offer amazing opportunities to help and empower people in the decades and centuries ahead.

Ethics and Values

  • Safety: AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.
  • Failure Transparency: If an AI system causes harm, it should be possible to ascertain why.
  • Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.
  • Responsibility: Designers and builders of advanced AI systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications.
  • Value Alignment: Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation.
  • Human Values: AI systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity.
  • Personal Privacy: People should have the right to access, manage and control the data they generate, given AI systems’ power to analyze and utilize that data.
  • Liberty and Privacy: The application of AI to personal data must not unreasonably curtail people’s real or perceived liberty.
  • Shared Benefit: AI technologies should benefit and empower as many people as possible.
  • Shared Prosperity: The economic prosperity created by AI should be shared broadly, to benefit all of humanity.
  • Human Control: Humans should choose how and whether to delegate decisions to AI systems, to accomplish human-chosen objectives.
  • Non-subversion: The power conferred by control of highly advanced AI systems should respect and improve, rather than subvert, the social and civic processes on which the health of society depends.
  • AI Arms Race: An arms race in lethal autonomous weapons should be avoided.”

As you can see, this is a lot more comprehensive, and it looks like we’re getting there. The only thing that’s missing is the involvement of the government, which is crucial for any of this to work. This is addressed by the guidelines arrived at by attendees of the New Work Summit that was hosted by The New York Times earlier this year.

New Work Summit

“Attendees at the New Work Summit, hosted by the New York Times, worked in groups to compile a list of recommendations for building and deploying ethical artificial intelligence:

  • Transparency: Companies should be transparent about the design, intention and use of their A.I. technology.
  • Disclosure: Companies should clearly disclose to users what data is being collected and how it is being used.
  • Privacy: Users should be able to easily opt out of data collection.
  • Diversity: A.I. technology should be developed by inherently diverse teams.
  • Bias: Companies should strive to avoid bias in A.I. by drawing on diverse data sets.
  • Trust: Organizations should have internal processes to self-regulate the misuse of A.I. Have a chief ethics officer, ethics board, etc.
  • Accountability: There should be a common set of standards by which companies are held accountable for the use and impact of their A.I. technology.
  • Collective governance: Companies should work together to self-regulate the industry.
  • Regulation: Companies should work with regulators to develop appropriate laws to govern the use of A.I.
  • “Complementarity”: Treat A.I. as tool for humans to use, not a replacement for human work.

After looking at this final list of guidelines, we’re still seeing a gap in how these issues will be addressed at various levels. The New Work Summit does cover collective governance and regulations, but fails to mention that regulatory bodies need a proper framework by which to guide the development of AI. What’s missing is that nobody is telling the government what it needs to do, and that’s the weakest link in the chain right now.

The America AI Initiative executive order signed by Trump earlier this year are as lacking of government accountability as the EU’s guidelines. Everybody seems to love telling everybody else what they should do, but offer very vague support for these initiatives. Trump’s order mentions nothing of where government agencies will get additional funding, but rather encourages them to reallocate spending. Not an easy pill for bureaucracy to swallow.

Governments in countries like the United States should be the ones taking the first step. They’re the ones who should be taking this bull by the horns and wrestling it to the ground. If AI is to remain subservient to humans, this is where it starts.

Unfortunately, that would require a tectonic shift in government policy itself, so don’t hold your breath. We’ll continue to muddle through for the next few years until a serious transgression by an AI entity brings everything to the forefront and makes it an urgent matter of international interest.

The question is, are we going to repeat history by waiting for something bad to happen before we react? To analogize, do we need a major global incident like WWII in order to set up a NATO? Can’t we be more proactive and setup a failsafe now when AI is still in its nascency?

These are the hard questions governments must answer because such a massive initiative requires financial and other resources that only governments can provide and control. There won’t be any lack of participants, but the participants cannot host the show.

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Source: https://1reddrop.com/2019/04/13/ai-ethics-guidelines-from-diverse-groups-the-consensus/

AI

Aite survey: Financial institutions will invest more to automate loan process

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Financial institutions plan to increase their spend on automations and collections management solutions for their loan processes. Fresh results on consumer lending practice from research and advisory firm Aite Group indicate lenders plan to invest more heavily in their collections processes, said Leslie Parrish, senior analyst for the Aite Group’s consumer lending practice. Parrish shared […]

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Source: https://bankautomationnews.com/allposts/lending/aite-survey-financial-institutions-will-invest-more-to-automate-loan-process/

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Facial recognition, other ‘risky’ AI set for constraints in EU

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Facial recognition and other high-risk artificial intelligence applications will face strict constraints under new rules unveiled by the European Union that threaten hefty fines for companies that don’t comply.

The European Commission, the bloc’s executive body, proposed measures on Wednesday that would ban certain AI applications in the EU, including those that exploit vulnerable groups, deploy subliminal techniques or score people’s social behavior.

The use of facial recognition and other real-time remote biometric identification systems by law enforcement would also be prohibited, unless used to prevent a terror attack, find missing children or tackle other public security emergencies.

Facial recognition is a particularly controversial form of AI. Civil liberties groups warn of the dangers of discrimination or mistaken identities when law enforcement uses the technology, which sometimes misidentifies women and people with darker skin tones. Digital rights group EDRI has warned against loopholes for public security exceptions use of the technology.

Other high-risk applications that could endanger people’s safety or legal status—such as self-driving cars, employment or asylum decisions — would have to undergo checks of their systems before deployment and face other strict obligations.

The measures are the latest attempt by the bloc to leverage the power of its vast, developed market to set global standards that companies around the world are forced to follow, much like with its General Data Protection Regulation.

The U.S. and China are home to the biggest commercial AI companies — Google and Microsoft Corp., Beijing-based Baidu, and Shenzhen-based Tencent — but if they want to sell to Europe’s consumers or businesses, they may be forced to overhaul operations.

Key Points:

  • Fines of 6% of revenue are foreseen for companies that don’t comply with bans or data requirements
  • Smaller fines are foreseen for companies that don’t comply with other requirements spelled out in the new rules
  • Legislation applies both to developers and users of high-risk AI systems
  • Providers of risky AI must subject it to a conformity assessment before deployment
  • Other obligations for high-risk AI includes use of high quality datasets, ensuring traceability of results, and human oversight to minimize risk
  • The criteria for ‘high-risk’ applications includes intended purpose, the number of potentially affected people, and the irreversibility of harm
  • AI applications with minimal risk such as AI-enabled video games or spam filters are not subject to the new rules
  • National market surveillance authorities will enforce the new rules
  • EU to establish European board of regulators to ensure harmonized enforcement of regulation across Europe
  • Rules would still need approval by the European Parliament and the bloc’s member states before becoming law, a process that can take years

—Natalia Drozdiak (Bloomberg Mercury)

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Source: https://bankautomationnews.com/allposts/comp-reg/facial-recognition-other-risky-ai-set-for-constraints-in-eu/

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Prioritizing Artificial Intelligence and Machine Learning in a Pandemic

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AI and ML
Illustration: © IoT For All

Artificial Intelligence (AI) and Machine Learning (ML) give companies the one thing humans can’t – scalability. Over time, humans limit a businesses’ ability to scale; they can only work so many hours at a given efficiency. On the other hand, AI and ML can work around the clock with the sole focus on a given project. As organizations navigate through COVID-19’s impact and the future of a remote workforce, scalability and efficiency can be the key to an organization’s successful recovery.

Implementation Challenges

The benefits of AI and ML don’t come without their own challenges; however, the top challenges are a lack of skills and time for proper implementation. In July, Deloitte found in a survey that 69% of respondents said the skills gap for AI implementation ranged from moderate to major to extreme. Simultaneously, many companies overlook the investment it takes to build the processes and infrastructure needed for successfully training, testing, deploying, and maintaining AI and ML in their enterprise.

Such challenges often cause companies to de-prioritize AI and ML projects, especially in times of uncertainty. That has been increasingly obvious throughout the COVID-19 pandemic. But while some organizations have drawn back on their efforts, the current global state demands the greater need for AI and ML to support critical business processes. This is especially true today given the growing remote workforce, considerations for returning to the workplace and work happening in silos worldwide.

Though challenging, it is not impossible to properly implement AI and ML. In this evolving COVID-influenced business landscape, four steps are key to effectively implementing a strong AI and ML system that helps streamline critical business processes despite uncertainty and limited resources.

Identify the Problem to Be Solved

Some companies mistakenly view AI and ML projects as a ‘silver bullet’ to solve all their problems. This often results in overinflated expectations, an unfocused approach, and unsatisfactory results. Instead, companies should identify those specific problems that will have the biggest impact from implementing AI and ML solutions and be hyper-focused on solving those problems.

Select Your Data

The second step in creating a strong AI and ML algorithm is to select the source data that your algorithm will be training on. There are two main options: training on your own data or training on a larger scale data set. Based on experience, training your algorithm on your own data puts you at a disadvantage. By training on a larger scale data set, the likelihood of success increases because your data is more representative and varied. Through advanced concepts such as transfer learning, companies can use semi-trained models based on larger data sets and then train the “last mile” using their own specific content unique to their business.

Clean House

The standby rules of data management apply here – garbage in, garbage out. Ultimately, the quality and accuracy of machine learning models depend on being representative. AI and ML – fed with the right data – can streamline operations and increase the benefit of companies’ DX and cloud migration journeys.

When you’re kicking off an AI or ML project, the most critical step is to clean up the data that your algorithm will be training on, especially if you’re using your own data or models.

Make Room for Training

AI and ML are all about probability. When you ask it a question, for example, “Is this a cat?,” the results you receive are the algorithm saying, “Out of the three buckets I was trained on, the likelihood of this image being a cat is .91, the likelihood of this image being a dog is .72 and the likelihood of this image being a bird is .32.”

This is why training on varied data is so important. If your training data only includes images of cats, dogs, and birds and you ask the algorithm to analyze the picture of a crocodile, it will only respond based on the buckets it’s been trained on – cats, dogs, and birds.

If you’ve properly selected and cleaned your data, training should be an easy last step, but it’s also an opportunity to go back to the first two steps and further refine based on your training.

The front end of training an AI and ML algorithm can be time-intensive, but following these four steps can make it easier to achieve significant outcomes. Across industries, AI and ML can quickly show ROI. For example, in the insurance industry, AI and ML can help insurers quickly search contracts, so employees aren’t sifting through contracts and repositories around the globe to answer simple questions. This means time efficiencies for an industry that COVID-19 has heavily impacted.

Even better, working with a SaaS provider with experience in your industry can make this process much easier and less costly. SaaS platforms allow companies to take advantage of having all of the infrastructure, security, and pre-trained models in place to reduce the overall effort and time to value. Many platforms allow users to uptrain the predefined models with unique customer data, reducing the training effort needed for model creation. Companies can then focus on integration with their ecosystem and workflows rather than model creation itself.

Bigger Picture

Overall, businesses can soften the impact of COVID by focusing on the bigger picture with AI and ML. Implementing AI and ML projects increase business productivity despite these times of uncertainty. As we continue on the road to recovery, we need tools like AI and ML to stay focused on the bigger picture, mission-critical tasks.

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Source: https://www.iotforall.com/prioritizing-artificial-intelligence-and-machine-learning-in-a-pandemic

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ProGlove promotes worker well-being with human digital twin technology

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Join Transform 2021 this July 12-16. Register for the AI event of the year.


ProGlove, the company behind an ergonomic barcode scanner, has developed new tools for analyzing human processes to build a human digital twin.

“We have always been driven to have our devices narrate the story of what is really happening on the shop floor, so we added process analytics capabilities that allow for time-motion studies, visualization of the shop floor, and more,” ProGlove CEO Andreas Koenig told VentureBeat.

The company’s newest process analytics tools can complement the typical top-down perspective of applications by adding a process-as-seen view to the conventional process-as-wanted view. Most importantly, it can also provide insights that improve well-being.

Koenig said, “We are building an ecosystem that empowers the human worker to make their businesses stronger.”

ProGlove CEO Andreas Koenig

Above: ProGlove CEO Andreas Koenig

Image Credit: ProGlove

The market for barcode scanning is still going strong and is often taken for granted, given how old it is. “You have technologies like RFID that have been celebrated for being the next big thing, and yet their impact thus far hasn’t been anywhere near where most pundits expected it,” Koenig said.

Companies like Zebra, Honeywell, and Datalogic have lasted for decades by building out an ecosystem of tools to address industry needs. “What sets us apart is that we looked beyond the obvious and started with the human worker in mind,” Koenig said.

Not only is the company providing a form factor designed to meet requirements for rugged tools, this shift to analytics could further promote efficiency, quality, and ergonomics on the shop floor.

How a human digital twin works

ProGlove’s cofounders participated in Intel’s Make It Wearable Challenge, with the idea of designing a smart glove for industries. Today, ProGlove’s MARK scanner can collect six-axis motion data, including pitch, yaw, roll, and acceleration, along with timestamps, a step count, and camera data (such as barcode reading speed and the scanner ID).

Koenig’s vision goes beyond selling a product to establish the right balance between businesses’ need for profits and their obligation to ensure worker well-being. Koenig estimates that human hands deliver 70% of added value in factories and on warehouse floors. “There is no doubt that they are your most valuable resource that needs protection. Even more so since we are way more likely to experience a shortage of human workers in the warehouses across the world than having them replaced by robots, automation, or AI.”

ProGlove Insight contextualizes the collected data and lets users compare workstations and measure the workload and effort necessary to complete the tasks. Users can also visualize their shop floor, look at heatmaps, and identify best practices or efficiency blockers. After a recent smart factory lab experiment with users, DPD and Asics realized efficiency gains by as much as 20%, Koenig said.

ProGlove’s vision of the human digital twin is built on three pillars: a digital representation of onsite workers, a visualization of the shop floor, and an industrial process engineer. “The human digital twin is all about striking the right balance between businesses’ needs for profitability, efficiency, and worker well-being,” Koenig said. At the same time, it is important that the human digital twin complies with data privacy regulations and provides transparency to frontline workers around what data is being transmitted.

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Source: https://venturebeat.com/2021/04/21/proglove-promotes-worker-well-being-with-human-digital-twin-technology/

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