AI, Bias and Law

Selection algorithms everywhere are exhibiting traits that appear to be racist, sexist, and otherwise discriminatory. Have neural networks already developed their own neuropathy? Or are people somehow the problem?

From insurance payments to courtroom sentencing, AI makes increasingly complex decisions about our lives. And our belief that data is neutral allows algorithms to get away with murder. The fight back is being led by those most likely to find themselves on the wrong side of a computer’s decision.

Artificial intelligence has already started to shape our lives in ubiquitous and occasionally invisible ways. A researcher in algorithmic bias talks to FRONTLINE about what she thinks the public needs

Algorithms are only as good as the people who make them.

Racism among humans is a social problem, but racism in an AI is an engineering problem.

Ahead of the launch of Google's smart speaker and smartphone, workers were placed in a room with a device and asked to repeatedly utter the most vile slurs that came to mind.

A proposed rule from HUD would make it harder to hold people accountable for subtler forms of discrimination.

Algorithms can reduce bias for three reasons.

The social network is exploring new product areas through a team dedicated to building the company’s future.

Sandra Wachter says AI uses sensitive personal traits to target or exclude people in ads

Despite its popularity in almost every field, AI still harbours human biases.

Study reveals rampant racism in decision-making software used by US hospitals — and highlights ways to correct it.

We keep stumbling across examples of discrimination in algorithms, but that’s far better than their remaining hidden.

Over the past few months, we’ve documented how the vast majority of AI’s applications today are based on the category of algorithms known as deep learning, and how deep-learning algorithms find patterns in data. We’ve also covered how these technologies affect people’s lives: how they can perpetuate injustice in hiring, retail, and security and may already be doing so in the criminal legal…

Doing so was inadvertent, but it taught me an important lesson

PAI research highlights a divergence between legal and machine learning terminology related to fairness and bias. These two communities must collaborate and align in order to effectively prevent bias and promote fair algorithmic practices. From racially disparate risk assessments in the criminal justice system to gender-discriminatory hiring decisions in the workplace, examples of potential biases …

In April 2019, Democratic lawmakers in the U.S. House of Representatives and the U.S. Senate introduced the Algorithmic Accountability Act (the “Act”). The proposed legis

Machines can discriminate in harmful ways. Here's how we fix the problem

A computer can make a decision faster. That doesn’t make it fair.