Few question the ability of AI and associated technologies like machine learning and deep learning to transform the world around us. The potential for these technologies to change industries, boost productivity, and positively impact employees’ relationship with work has new AI solutions arriving on the market each day. But consumers are hesitant.

From the future of AI and humans’ role in it to the ethical and practical standards of adding AI to your business workflows, many questions still need to be answered. Businesses need to be able to trust and verify AI’s work product before making large-scale investments and repurposing resources.

AI Training and Data Utility

Chief among concerns are AI tools generating biased results. Biases can creep into results in several ways, including the use of deliberately skewed datasets to generate desired outcomes. Some biases are accidental but they are almost always the result of flawed data sets that are either not large enough or not statistically representative.

The results are AI systems that contain one or more biases common to data-driven computing. For patent search and data analytics powered by NLP, input data should reflect a variety of domains and language contexts, with plenty of semantically linked ideas expressed. NLP datasets should capture intent as well as a wide range of language uses. For patent search platforms, it is important that results are accurate, fair, and independent.

AI systems must be ‘trained’; that is, large datasets are integrated to inform an AI’s output. This process is largely controlled by humans, so our biases easily creep in. While most AI developers strive to incorporate the largest, highest quality datasets possible, quality data can be difficult to acquire and process. Issues of gender and racial bias rightfully headline these challenges. While not an immediate concern of IP.com’s application of artificial intelligence to IP, these pitfalls starkly demonstrate the risks of bad data.

Trust Results and Avoid Bias

While bias is prevalent, it can be avoided. To avoid the data that result from historical, confirmation, selection, and availability biases, steps can be taken to both control for these factors and mitigate their impact on an algorithm.

1. Understand assumptions. When building an algorithm, certain assumptions are made about its application and the data used to train it. Account for these in human analysis of output metrics and consider whether assumptions have an outsized influence on outcomes.
2. Consider domestic AI options. For AI to be ethical, it should align with the values of the company using it. US-based AI options are typically more transparent and more readily respond to an individual company’s needs.
3. Apply AI narrowly. To reduce the ethical and practical risks of AI, companies are choosing to apply it more narrowly than to holistically transform systems overnight. IP.com’s innovation suite has a number of ways to apply AI narrowly in terms of idea assessment, search and analytics, or landscape analysis.
4. Prepare for contingencies when implementation is problematic. Understand what risks are within the scope of an AI application. Do they pertain to security, bias, or unreliable results?

Limiting Bias and Ethical Concerns on IP.com’s Platform

1. Use only high quality data sets. Use a collection of datasets from multiple sources that capture the use of language in a variety of semantic contexts to best understand search intent.
2. Train in-house. Our semantic AI has been trained for years by our internal engineers who go above and beyond industry standards to control the quality of our NLP training data.
3. Ensure results are independent of outside influence. AI platforms can directly or indirectly lead to an undue preference for results to dishonestly validate proprietary claims or favor third parties. IP.com’s only standard is that data is objective and useful for its users.
4. Avoid security concerns. Data entered into search and analysis modules is privately stored and shared with no one. Our cloud-based software takes into account potential security threats from malicious actors.
5. Always be testing. Our industry-leading NLP algorithm is always being refined and improved with the latest training data.

AI-powered Innovation Workflows are Becoming the Norm

• It costs an average of $56,000 to file a patent in 2022.
• AI-powered search delivers results in 80% less time.
• The average cost of one freedom to operate search without AI is $10,000 in 2022.
• Using AI-enhanced IP search boosts success by 20%.

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• Source: https://ip.com/blog/avoiding-the-pitfalls-of-ai-implementation/