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What are the inherent biases in AI?

Artificial intelligence (AI) is a powerful technology that can enhance human capabilities, automate tasks, and solve complex problems. However, AI is not a neutral or objective tool. It reflects the values, assumptions, and biases of its creators and users. In this blog post, we will explore some of the inherent biases in AI, how they affect different groups of people, and what can be done to mitigate them.


Bias is a deviation from fairness or accuracy in judgement, decision-making, or behaviour. Bias can be intentional or unintentional, conscious or unconscious, explicit or implicit. Bias can also be embedded in data, algorithms, systems, or processes. Bias can have negative impacts on individuals and society, such as discrimination, exclusion, injustice, or harm.


Some of the common sources of bias in AI are:


  • Data bias: This occurs when the data used to train or test an AI system is not representative of the real-world population or scenario that the system is intended to serve. For example, if an AI system for facial recognition is trained on a dataset that is predominantly composed of white male faces, it may perform poorly on faces of other races or genders. Data bias can also result from data collection methods that are skewed, incomplete, outdated, or inaccurate.
  • Algorithmic bias: This occurs when the rules or logic of an AI system produce unfair or inaccurate outcomes for certain groups of people or situations. For example, if an AI system for credit scoring assigns lower scores to applicants based on their zip code, race, gender, or other factors that are not relevant to their creditworthiness, it may result in unfair denial of loans or higher interest rates. Algorithmic bias can also result from flawed design choices, optimization goals, evaluation metrics, or feedback mechanisms.
  • Human bias: This occurs when the human creators or users of an AI system introduce their own preferences, beliefs, stereotypes, or prejudices into the system. For example, if a human recruiter uses an AI system to screen job candidates based on their resumes and social media profiles, they may unconsciously favour candidates who share their background, culture, or ideology. Human bias can also result from lack of awareness, education, diversity, or accountability.


Bias in AI can have serious consequences for individuals and society. Some of the potential harms of biased AI are:


  • Discrimination: Biased AI can discriminate against certain groups of people based on their characteristics or attributes that are protected by law or ethics. For example, biased AI can deny access to opportunities, resources, services, or benefits based on race, gender, age, disability, religion, sexual orientation, or other factors.
  • Exclusion: Biased AI can exclude certain groups of people from participating in or benefiting from the development and use of AI. For example, biased AI can create barriers to entry or access for people who do not have the required skills, knowledge, devices, connectivity, or language to interact with AI systems.
  • Injustice: Biased AI can violate the principles of fairness and equity that are essential for a just and democratic society. For example, biased AI can undermine the rule of law and human rights by enabling surveillance, censorship, manipulation, or oppression.
  • Harm: Biased AI can cause physical, emotional, psychological, or social harm to individuals or groups of people. For example, biased AI can endanger health, safety, privacy, dignity, or well-being by producing inaccurate, misleading, or harmful information, advice, or actions.


To prevent and reduce bias in AI, we need to adopt a multidisciplinary and collaborative approach that involves various stakeholders, such as researchers, developers, users, regulators, auditors, and advocates. Some of the possible strategies to mitigate bias in AI are:


  • Data quality: We need to ensure that the data used to train and test AI systems is accurate, complete, relevant, timely, and diverse. We also need to collect data in a transparent and ethical manner that respects the rights and consent of data subjects.
  • Algorithmic transparency: We need to make the rules and logic of AI systems clear and understandable to humans. We also need to explain the inputs, outputs, and outcomes of AI systems in a simple and accessible way that builds trust and confidence.
  • Human oversight: We need to monitor and evaluate the performance and impact of AI systems on a regular basis. We also need to provide mechanisms for human intervention and correction when AI systems produce unfair or harmful results.
  • Ethical principles: We need to adhere to the ethical principles and values that guide the development and use of AI. We also need to ensure that AI systems respect human dignity, autonomy, diversity, and rights.


Bias in AI is a complex and challenging issue.
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