The Rise of Algorithmic Decisions

The Unseen Power of Math

Today, algorithms often determine what college we attend, if we get hired for a job, if we qualify for a loan to buy a house, and even who goes to prison and for how long. Unlike human decisions, these mathematical models are rarely questioned. They just show up on somebody’s computer screen and fates are determined.

The Errors of Algorithms

In some cases, the errors of algorithms are obvious, such as when Dow Jones reported that Google was buying Apple for $9 billion and the bots fell for it or when Microsoft’s Tay chatbot went berserk on Twitter – but often they are not. What’s far more insidious and pervasive are the more subtle glitches that go unnoticed, but have very real effects on people’s lives.

The Consequences of Algorithmic Bias

Once you get on the wrong side of an algorithm, your life immediately becomes more difficult. Unable to get into a good school or to get a job, you earn less money and live in a worse neighborhood. Those facts get fed into new algorithms and your situation degrades even further.

The Need for Transparency and Accountability

That’s why it’s imperative that we begin to take the problem of AI bias seriously and take steps to mitigate its effects by making our systems more transparent, explainable, and auditable.

A Path Forward

We must work towards creating algorithms that are fair, equitable, and transparent. This requires a deeper understanding of the data we use to train these models, as well as the potential biases that may be present. It also requires us to be more proactive in testing and monitoring our algorithms to identify and correct any errors or biases that may arise.

Conclusion

The rise of algorithmic decisions has significant implications for our society. It is crucial that we take a proactive approach to ensuring that these decisions are fair, transparent, and accountable. By doing so, we can create a more just and equitable society for all.

FAQs

Q: What is algorithmic bias?

A: Algorithmic bias refers to the unintentional and often invisible biases that can be present in the data and code used to train algorithms, which can result in discriminatory outcomes.

Q: How prevalent is algorithmic bias?

A: Algorithmic bias is a widespread problem, affecting many areas of our lives, from hiring and education to lending and criminal justice.

Q: How can we mitigate algorithmic bias?

A: We can mitigate algorithmic bias by making our systems more transparent, explainable, and auditable, as well as by using diverse and representative data to train our algorithms.