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 ...

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.

Social media continues to be a powerful marketing tool for companies in the mortgage industry. Whether you are large or small, it’s incredibly important to have an established presence across all ...

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

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

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.

For example, the A-level algorithm adjusted results to try to replicate the previous overall achievements of different ethnic groups, which are likely to reflect racial inequality.

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 ...

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 ...

In another example, a commonly used algorithm for predicting the success of vaginal birth after a prior cesarean (VBAC) delivery predicts lower success for Black and Hispanic mothers relative to ...

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.