Challenges in Trusting AI Decisions

Factors undermining trust in AI solutions

Building trust in AI systems is crucial for their widespread adoption. Responsible AI practices that prioritize fairness, transparency, and user-centric design are essential for fostering trust among users, stakeholders, and the public.

In this lesson we focus on some key factors that undermine building trust in AI solutions.

Bias in AI decisions

The presence of bias in AI models is one of the key reasons that undermine trust in AI solutions.

Recently there have been many real-world situations where the existence of bias in AI solutions was brought to light.

• Facial recognition technology: Many facial recognition systems exhibit racial bias. These systems frequently give incorrect results for individuals with darker skin tones.

• Hiring algorithms: AI-powered hiring algorithms have been found to be biased against certain demographics. They were found to discriminate on the basis of gender and race.

• Criminal justice systems: AI systems used for predicting recidivism and determining sentencing have been found to display racial bias. These systems falsely flagged individuals from certain racial or ethnic groups as being at higher risk of reoffending.

• Access to financial services: AI-driven credit scoring algorithms have shown bias against certain demographics, resulting in unequal access to financial services. If individuals from marginalized communities are ...