Tackling Bias to Make AI Fair and Effective
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Loading... - Clara Andersen
- 02 May 2023
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As the capabilities of artificial intelligence (AI) continue to grow, more businesses across varied sectors seek to access the possibilities AI can offer. With AI’s reach expanding further than ever, it is critical for businesses and software professionals to be aware of the largest problem facing AI today: bias. Algorithm bias reflects partiality in people and systems and can lead to serious real-world consequences of perpetuating discrimination against already marginalized groups. It can also create a major liability for organizations, resulting in social and economic costs. Addressing AI bias requires large and varied data sets, thorough testing, and strong collaboration between developers and teams.
Causes of Data Bias
Bias in AI algorithms begins with the data. It involves systematic and unfair favoritism or discrimination against groups or individuals. Often, AI is biased because the sampling data it utilizes to train reflects historical discrimination. For example, an algorithm meant to select potential hires from a pool of candidates might train on documentation of previous selection decisions. If past hiring managers demonstrated a bias against female applicants, that bias would be revealed in the data, and the resulting algorithm would show the same tendency.
Another contributing factor in bias stems from incomplete, unrepresentative data. For instance, training a facial recognition algorithm on pictures of only white men will lead to its inability to recognize women and people of color. To avoid bias, sampling data sets must be large and varied enough to reflect the real world. Sets too small or drawn from a single source are more likely to be biased. Labels are also an important part of sampling data. If some data has incorrect or missing labels, the algorithm is more likely to make mistakes. Finally, context is essential for sampling data. If data lacks necessary context—for example, readings of a health metric that don’t account for age—this can contribute to the AI developing a misleading view of the world, thereby producing inaccurate results. Read More…
