Artificial intelligence is ever more creeping into our lives from detecting credit card fraud, dating sites, and Google searches. However, the big question is whether we can believe in the algorithms that drive this technology.

Humans are prone to errors; for instance, you can make a wrong interpretation or experience lapses in maintaining your attention. Once you realize that you have made a mistake, you can reassess and make amends. On the other hand, AI systems will keep making the same mistake for as long as the circumstances and data are given remain constant. The concept that drives AI technology relies on data that reflects the past. This is to means that if the systems are fed with data that comprises past human decisions and inherent biases, they will be amplified. Moreover, there is no doubt AI has changed how the world functions, so it is a challenge for developers to get it right.

How Does Algorithmic Bias Arise?

Algorithmic bias occurs due to several reasons. It could be as a result of inappropriate configuration or design of the system or absence of training data, among others. For example, a system used by financial institutions to advance credit will be programmed by using huge amounts of data. A client trying to apply for the loan will be analyzed against previous applicants. Ideally, the algorithm will rely on previous clients’ demographic information, employment history, and financial flow to establish whether the new client can meet the repayment terms. Additionally, algorithms can be racist and sexist as they can be biased based upon who built the system.

The above scenario is very problematic because it raises unconscious biases from loan executives who made decisions in the past. There is a possibility that minority groups were judged unfairly in the past. Other groups that may be disadvantaged based on this unfair judgment include people with disabilities, single women, and people of color.

Companies That Are Making Strides to Solve Biases Caused By AI

Several companies and organizations have identified the social problems caused by AI bias and have adjusted their data to help solve these issues. For example, Google is using AI to tackle social and environmental challenges across the world. Wadhwani Institute is another recognizable company in Mumbai that has successfully used AI to help small-scale traders. The company uses AI to fill in the gaps in large agriculture systems.

In the entertainment industry, Indian online casinos are using AI data from players’ history to recommend games tailored to a player’s preference. Ideally, if a player spends more time on online guides on how to play Baccarat online, the suggested games will be baccarat variations. Luckily with such guides, you can get advice on the best Baccarat sites in India, the bonuses available for Indian players, including the welcome offers. You also get a detailed outline of how to play Baccarat if you are a new player.

Mitigating Algorithmic Bias

It is possible to have AI systems that are devoid of bias. This technology can be built with the ethical principle that will accommodate the plights of people of different gender, age, and race. Here are a few ideas that can help ensure AI systems are fair and accurate.

Get Better Data: Algorithmic bias against minority groups can be eliminated through additional data points. For example, new data about these groups can be generated so that they are handled independently away from the trends in the past.

Pre-Process the Data: Before data is fed into the AI systems, it must be edited to clear any attributes that border anti-discrimination laws.

Increase Model Complexity: Many experts tend to develop simple AI models that are easy to interrogate and that use generalized data. Complex models will eliminate biases by incorporating all the needs of each group.

Modify the System: Governing parameters and logic can be adjusted to counter the biases. For instance, the decision threshold can be altered for a disadvantaged group.

Change the Prediction Target: The pattern used by AI systems to make decisions is developed using a specific measure. If the prediction target used is not yielding desired results, a fairer measure can be installed.