AI Missteps and Key Players
Reports of incidents related to AI underscore the need to address bias and discrimination in AI systems. Companies such as Facebook, Tesla, and Google are often mentioned in these reports. For example, Google Ads displayed high-paying job ads less frequently for women and minority groups. Similarly, an investigation found that the Apple Card algorithm granted lower credit limits to women compared to men, despite both groups having equal qualifications. Such incidents highlight the importance of ensuring fairness in AI systems.
Business Risks and Consequences
The existence of bias and discrimination in AI systems can have serious consequences for businesses, including missed opportunities, reputational damage, and legal issues. For instance, a recommendation system that only targets a specific user group may exclude potential customers, resulting in revenue losses. Additionally, biased AI systems can tarnish a company’s image, especially when dealing with sensitive societal issues. Moreover, in regulated industries such as the labor market, discriminatory AI algorithms can lead to legal consequences, including fines and sanctions under anti-discrimination laws.
Building Trustworthy AI for the Future
To minimize the risks of biased AI systems, it is important to develop and deploy trustworthy AI. This requires implementing measures to identify, mitigate, and prevent biases in AI algorithms. Companies should use diverse and inclusive datasets, implement transparent and accountable processes, and encourage interdisciplinary collaboration. By prioritizing fairness and inclusivity in the development of AI technologies, companies can not only enhance their reputation but also contribute to creating a more just society.
We support companies in implementing and utilizing trustworthy AI. Additionally, we conduct research on the measurability of fairness and trustworthiness in AI, which will be a crucial foundation for the certification and validation of AI according to the EU AI Act.
Learn more about our findings on bias in AI in our joint White Paper with SGS: Link to the White Paper