Are you in need of making a logical decision today? It’s likely that your biases, whether subtle or blatant, will impact your objectivity.

Cognitive bias refers to a systematic deviation from norm or rationality in judgment.

Image Credit: Wikipedia’s complete (as of 2016) list of cognitive biases, arranged and designed by John Manoogian III (jm3).

There are over 180 documented biases that can influence our daily decision-making, from hiring to personal choices.

Human biases and AI

Decisions made by cognitive systems rely on past knowledge and experience, just like human decisions. Both conscious and subconscious biases can affect the choices we make.

Also Read: The real-world bias issues of AI

Recruitment is an area where biases are prevalent and impact a significant part of society. Despite efforts to make the process fair, biases still exist, influenced by historical and societal factors.

Advocates of automated decision-making argue that algorithms can reduce biases by eliminating human influence. However, flaws in algorithms, such as those used in criminal risk assessment, show that biases can still be present.

Algorithmic biases

Data-driven algorithms combine human logic with data learning to automate processes efficiently. However, biases can enter the system through historical data, impacting decisions.

Also Read: These Artificial Intelligence startups are proving to be industry game-changers

Biases can also stem from the design of algorithms or lack of diversity in data sets, affecting decisions in areas like hiring or clinical trials.

Can cognitive bias be completely avoided?

Unlikely, as our minds tend to rely on automatic processing for efficiency. However, recognizing and correcting biases can help us make better decisions.

Explainable AI

To ensure transparency and accountability in decision-making, explainable AI aims to provide insights into algorithmic decisions. Understanding why algorithms make certain choices helps in reducing biases.

Awareness of the importance of explainability is crucial for end-users to demand unbiased and transparent algorithms in decision-making processes.

As AI continues to play a significant role in various sectors, addressing algorithmic biases is essential for fair and inclusive outcomes.

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