For example, an algorithm called CB (color blind) imposes the restriction that any discriminating variables, such as race or gender, should not be used in predicting the outcomes.

Can we ever really trust algorithms to make decisions for us? Previous research has proved these programs can reinforce society’s harmful biases, but the problems go beyond that. A new study ...

How, then, can a single algorithm guide different robotic systems to make the best decisions to move through their surroundings?

Though meant to make decisions around criminal justice, policing and public service easier, some are concerned algorithms designed by humans come with inherent bias and a need for oversight.

Under the right circumstances, algorithms can be more transparent than human decision-making, and even can be used to develop a more equitable society.

A special category of algorithms, machine learning algorithms, try to “learn” based on a set of past decision-making examples.

Making Algorithms More Like Kids: What Can Four-Year-Olds Do That AI Can’t? Thomas Hornigold Jun 26, 2019 Instead of trying to produce a programme to simulate the adult mind, why not rather try to ...

Artificial intelligence (AI) and algorithmic decision-making systems — algorithms that analyze massive amounts of data and make predictions about the future — are increasingly affecting Americans’ ...

Algorithms are embedded into our technological lives, helping accomplish a variety of tasks like making sure that email makes it to your aunt or that you're matched to someone on a dating website ...

There are three key reasons why predictive algorithms can make big mistakes. 1. The Wrong Data An algorithm can only make accurate predictions if you train it using the right type of data.

For example, an algorithm called CB (color blind) imposes the restriction that any discriminating variables, such as race or gender, should not be used in predicting the outcomes.