Managing Attorney, Compliance Counsel Law Group; Author of the books "Bold But Cautious" and "Chasing Corporate Compliance", available at Amazon and Barnes & Noble
Legal Risks with AI – Can Too Much Data Lead to Biased Business Practices? 🌟 As artificial intelligence (AI) becomes increasingly integrated into business operations, companies must navigate complex legal risks, especially concerning bias and discrimination. Regulators such as the Federal Trade Commission, are increasingly focused on the potential for AI to cause harm through biased decision-making. The Equal Employment Opportunity Commission has already warned that AI tools used in hiring processes must comply with anti-discrimination laws. European regulators are also tightening AI regulations under the EU’s General Data Protection Regulation. Data Bias AI systems are often trained on historical data, which may contain implicit biases. If this data reflects past discriminatory practices, AI can perpetuate those biases. For example, AI will naturally rely on proxies in its decision-making that can discriminate on the basis of age, race, or gender. Proxies are characteristics or data points that are not directly related to age, race or gender, but can be used to make inferences about it. Whether a loan applicant owns a Mac or PC, or their type of phone, or which store credit accounts appear on their credit history, can not only be indicators of a person’s credit repayment patterns, but can also be indicators of a person’s age, race or gender. Algorithmic Explainability AI transparency is no longer optional—it is a regulatory expectation. Companies must be prepared to explain their AI algorithm decision-making processes, and any measures taken to ensure fairness and compliance, especially when it involves lending, hiring, or healthcare. When it comes down to it, is large-scale data truly needed to make a predictive credit or hiring decision? Just because there is a possible statistical relationship does not mean that it is predictive of anything. AI systems often identify patterns and correlations in vast datasets. However, correlation does not equal causation—just because two variables are statistically related does not mean one predicts or influences the other. For example, an AI might find a correlation between a candidate’s zip code and job performance, but using such data in hiring could reflect socioeconomic bias, not true predictive power. Businesses must carefully validate AI findings and ensure that the relationships identified are backed by domain knowledge and practical relevance, not just data-driven coincidences. Blind reliance on AI without understanding the context can result in poor decision-making and legal risks. Explore much more in “Bold But Cautious” Available at Amazon and Barnes & Noble. Amazon: https://lnkd.in/g7NgNRUr Barnes&Noble: https://lnkd.in/gqNvY9bi Compliance Counsel Law Group: https://lnkd.in/gEqybFuc
