Page 76 - CT_AI_Class-7
P. 76
Algorithmic bias: Algorithmic bias happens when the design of the AI system itself causes
unfair decisions. This can happen if the AI system focuses too much on one factor and ignores
others. For example, in a video game, an AI system might reward players who make quick
decisions, but not those who take their time to think carefully. This would be unfair to players
who play thoughtfully. Algorithmic bias happens when the system favours one style of play
over another.
Human bias: AI systems are created by people and if the people designing the AI have their
own biases, those biases can affect the AI. For example, if someone who loves rock music
creates an AI that suggests songs, it might suggest rock music to everyone, ignoring other
types like pop, classical or jazz. This is human bias, where the personal preferences of the
creator influence the AI’s decisions.
21 st
Century #Critical Thinking
ai in action Skills
The AI Bias Game shows how bias can affect decision-making. In this game, you work for
the World Sports Board and need to pick lucky fans to win free tickets to a game. You will
receive test results from different people who claim to be sports fans. Visit the given link
https://ai-bias.sustainablelivinglab.org/ or scan the OR code to play the game):
How Bias Affects AI
When AI systems are biased, they can make unfair decisions. This can cause harm, especially in
important areas like healthcare, education and jobs. Here are some examples of biased decisions
in AI:
Biased decisions when giving loans: An AI system could unfairly deny someone a loan
because it was trained on biased data.
Unfair hiring or recruitment decisions: If AI is used to decide who gets a job, it might be unfair
if it’s trained on data that favours certain groups of people over others.
Wrong predictions about crime risks: If AI is used to predict crime, it might unfairly target
certain communities based on biased data.
Incorrect medical or health advice: If AI systems are used to give health advice, they might
make wrong suggestions if the data is biased or incomplete.
Mistakes in identifying people: Facial recognition systems can make mistakes, especially if
they haven’t been trained on diverse data, leading to wrong identifications.
CASE EXAMPLES OF AI BIAS
Artificial intelligence (AI) is being used in many areas of lives, from hiring people to helping doctors.
But sometimes, AI systems can be unfair. This happens when the data used to train these systems
has hidden biases or when the system is not designed properly. These biases can cause the AI to
make unfair decisions, which can affect certain groups of people more than others. Let us explore
few examples of AI bias.
74 Artificial Intelligence (CT & AI)-VII
