Page 62 - Ai V2.0 Flipbook C8
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Ethical Issues with AI
Let us now understand different ethical issues using AI.
Bias and Discrimination
AI systems can inherit biases present in their training data, leading to unfair outcomes. For
example, facial recognition software has been found to have higher error rates for people with
darker skin tones. It is crucial to ensure AI systems are trained on diverse and representative data
to avoid discrimination.
Privacy violations
AI systems often require large amounts of personal data. Without proper safeguards, this data
can be misused, leading to privacy breaches. It is important to implement strong data protection
measures and ensure transparency in data usage.
Lack of transparency
Many AI systems operate as “black boxes”, meaning their decision-making processes are not
easily understood. This lack of transparency can lead to mistrust and accountability issues.
Developing explainable AI is essential for building trust.
Job displacement
Automation through AI can lead to job losses in certain sectors. While AI can create new
opportunities, it is important to consider the social impact and provide support for affected
workers.
Security risks
AI systems can be vulnerable to attacks, such as data poisoning or adversarial inputs, which can
manipulate their behaviour. Ensuring robust security measures is vital to protect AI systems from
malicious activities.
Autonomous weapons
The development of AI-powered weapons raises ethical concerns about accountability and the
potential for misuse in warfare. It is important to establish international regulations to govern
the use of such technologies.
Accountability
Determining who is responsible when an AI system causes harm is a complex issue. Clear
accountability structures are necessary to ensure that individuals or organisations can be held
liable for the actions of AI systems.
60 Touchpad Artificial Intelligence (Ver. 2.0)-VIII

