Impact of data bias on decision-making

Conquering Data Bias

Konstantinos Kattidis

Data Analytics Lead

Bias and data ethics

• Decisions are only as reliable as the data behind them
• When data is not aligned with the intended objectives, it can become a matter of data ethics
• It covers the ethical and moral obligations of collecting, analyzing, and using data
• It tackles issues such as discrimination, privacy, transparency, and accountability.

Unfair treatment and discrimination

• Data bias can lead to unfair treatment of certain individuals or groups
• Biased data can result in discriminatory outcomes
• For instance:
  • Biased sampling of customer purchase data may inadvertently disadvantage certain demographics
  • Resulting in biased marketing strategies

Reinforcement of stereotypes

• Models trained on biased data may learn and reinforce existing societal biases
• Suppose the training data of a recommendation algorithm predominantly features content from a specific political ideology
• The algorithm, might recommend content that aligns with the dominant political ideology

Loss of trust and legal issues

• When biased decision-making becomes known, it erodes trust in the data
• In worst cases, it leads to legal and ethical challenges
• It can result in legal action and damage to an organization's reputation

Hindering performance and innovation

• Bias can lead to suboptimal decision-making and hinder performance and innovation
• For instance:
  • Lower utilization rates of a particular team occur due to bias
  • This leads to misguided performance evaluations
  • Impacting resource allocation, process efficiency and innovation

The feedback loop of bias

• Biases can create a feedback loop where historical biases are reinforced and perpetuated over time
• As biased decisions are made, they contribute to new biased data, which further influences future decisions

Let's practice!

Conquering Data Bias