Create Global Framework For Ethical Use Of AI, Says PM Modi At B20 Summit

Relevance: GS3 - Awareness in the fields of IT & Computers

(Source: Indian Express, 08/28/2023)

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Why in the news?

• Recently, Prime Minister Narendra Modi addressed the Business 20 (B20) Summit, organized by the Confederation of Indian Industries (CII).
• He stressed the need for a global framework to ensure ethical usage of artificial intelligence (AI) and also flagged concerns over algorithmic bias and its disruptive impact on society.

What is Algorithmic Bias?

• Algorithmic bias is the phenomenon where algorithms, which are sets of rules and instructions designed to make decisions or predictions, produce unfair or discriminatory outcomes.
• These biased outcomes can arise due to the way algorithms are created, the data they are trained on, or the assumptions embedded in their programming.
• This bias can lead to unjust and undesirable consequences, such as reinforcing stereotypes, exacerbating societal inequalities, and hindering equal access to opportunities for different groups of people.

Examples that emphasize the importance of addressing Algorithmic Bias

• Job Hiring and Recruitment: Resume screening algorithms can inadvertently favor certain demographic groups over others, based on historical hiring patterns. If the algorithm learns from resumes that have been submitted and selected in the past, it may perpetuate existing gender or racial biases in the job market.
  • For example: AI Recruiting Tool developed by Amazon to streamline the hiring process was found to be biased against female applicants. The tool favored male candidates due to the male-dominated resumes used during training.
• Racial Bias in Healthcare Algorithms: A study published in Science revealed that a widely used algorithm for predicting which patients would benefit from additional medical support was biased against black patients, leading to racial disparities in healthcare access.
• Online Advertisement: Algorithmic bias can also affect the ads individuals see online. If ad platforms use biased data to determine which ads to display, it can reinforce stereotypes and limit opportunities for certain groups, like showing high-paying job ads to men more frequently than women.
• Sentencing Recommendations: Algorithmic tools used to suggest sentencing for criminals can be biased. Biased historical data can result in recommendations that disproportionately impact minority groups, perpetuating inequality in the criminal justice system.
  • For example: COMPAS Risk Assessment Tool used by the U.S. judicial system, was found to be biased against African Americans due to the algorithm's pre-processing techniques.

Impact of Algorithmic Bias

• Social Inequity: Algorithmic bias can reinforce and perpetuate existing social inequalities. If historical data used to train algorithms contains biases, the algorithms can inadvertently learn and perpetuate those biases, leading to discriminatory outcomes in areas such as lending, hiring, and criminal justice.
• Discrimination: Bias in algorithms can lead to discriminatory outcomes based on factors like race, gender, age, and other protected characteristics. This can result in individuals from marginalized groups being unfairly denied opportunities, resources, or services.
• Fairness and Equal Treatment: Algorithmic bias undermines the principles of fairness and equal treatment. It can lead to situations where individuals with the same qualifications or characteristics receive different outcomes solely due to their demographic background.
• Privacy Concerns: Biased algorithms can compromise individuals' privacy by making inaccurate assumptions about their behaviors or preferences based on biased data. This can lead to privacy violations and the propagation of misleading information.
• Economic Impact: Unfairly biased algorithms can limit economic opportunities for individuals and groups, affecting their ability to access financial services, education, and employment opportunities. This can contribute to a cycle of poverty and economic inequality.

Potential Solutions to Mitigate Algorithmic Bias

• Diverse and Representative Data: Using diverse and representative data to train algorithms can mitigate bias by avoiding the perpetuation of historical inequalities.
• Bias Detection and Mitigation: Developing techniques to identify and mitigate bias in algorithms is crucial. This involves regular audits of algorithmic decision-making systems.
• Transparency and Accountability: Making algorithms transparent and understandable can help identify and correct biases. Organizations should be held accountable for biased outcomes.
• Regular Audits: By conducting regular audits of algorithmic systems can help to identify and rectify biases.

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FAQs

MCQ

Question: What does algorithmic bias refer to in the context of AI systems?

(a) The speed at which algorithms make decisions.

(b) The size of datasets used in algorithm training.

(c) Unfair outcomes resulting from algorithm design and deployment.

(d) The complexity of algorithms used in AI applications.

Question: With the present state of development, Artificial Intelligence can effectively do which of the following? (UPSC 2020)

1. Bring down electricity consumption in industrial units
2. Create meaningful short stories and songs
3. Disease diagnosis
4. Text-to-Speech Conversion
5. Wireless transmission of electrical energy

Select the correct answer using the code given below:

(a) 1, 2, 3 and 5 only

(b) 1, 3 and 4 only

(c) 2, 4 and 5 only

(d) 1, 2, 3, 4 and 5