06 December 2022

MAINS QUESTIONS

Q1. What do you understand by Political Gaslightning? Also discuss the associated issues and potential solutions?

 Paper & Topic: GS II – Election related issues

Introduction:

• “Gaslighting” will be the word of the year in 2022, according to Merriam-Webster. The expression, which was initially intended to characterise interpersonal deceptions, has recently gained in popularity. These lies, however, are spreading and having an impact on both domestic and international politics in this post-truth period.

Political gaslighting: what is it?

• Gaslighting is the practise of tricking someone into doubting their perception of reality.
• Gaslighting has been a common political tactic in the era of readily circulated fake news and tailor-made “facts.”
• For instance, candidates who are defeated in elections charge that the system is unjust.
• Concerns about government initiatives to restrict free expression are dismissed as the product of malicious foreign actors spreading lies.

How does that work exactly?

• False information is frequently used in political gaslighting to undermine and confuse the public’s understanding of important political topics. As a result, a position, idea, or programme may face increased support or resistance.
• As a result, bogus stories that are unfounded in reality are created.
• This is accomplished through the use of both words and power, both of which profit from injustice.

 The two types of political gaslighting are as follows:

• Overt political gaslighting that involves disseminating false information to the public Politicians that disagree with events like the destruction of the Babri Masjid are examples.
• When a speaker makes unsupported claims that are impossible to check or refute, they are subtly engaging in political gaslighting. Crocodile tears and manipulating people’s emotions in response to some violent protests are two instances.
• Victims: The gaslighter labels anyone who challenges these other stories as unreasonable or psychologically ill.
• Those who are impacted by this political manoeuvre fall into two categories:
• Gaslighters who have either been gaslighted themselves or have been persuaded to do so routinely silence journalists and academics.

 Examples:

• Germany under the Nazis witnessed some of the earliest instances of meticulously planned and regulated political gaslighting. Dissidents were, for instance, charged with forging letters and other cunning tactics.
• Already, the tactic has been employed to intensify racial conflict and even to alter history.
• During the pandemic, political gaslighting dramatically grew as certain officials called COVID-19 a “conspiracy” and made attempts to minimise the severity of the issue. In other instances, officials pronounced an early triumph against the virus in an effort to boost their political status and utilised contentious matters to divert attention from the public health emergency.
• It’s probable that not all occurrences of “political gaslighting” fall under this category. This includes things like spreading purposefully false information, downplaying government misconduct, defaming political competitors, deflecting attention from important topics, etc.

Why is that an issue?

• The detrimental effects of political gaslighting became brutally clear during the pandemic. For instance, people broke COVID-appropriate norms by downplaying the virus’s risk, which increased the number of cases and fatalities.
• Such tactics also limit the public’s ability to evaluate a programme objectively and appreciate its immediate and long-term impacts.
• This eventually leads to mistrust among the broader populace.

 What should we do next?

• Political gaslighting is a significant hindrance to progress on a global scale as well as a significant contaminant of public discourse. It has benefited everything from violent crime to societal divisiveness.
• Encourage critical thinking to identify and protect the audience from political gaslighting as a general rule. Education and fact-checking would be more beneficial in this regard.
• Truth is frequently cited as the cure for political gaslighting, but what is actually required is a democracy that rejects both truth and post-truth. The key is to encourage a “monitory” democracy, or one that fosters the expansion of public spaces that oppose and restrain state excesses.

Conclusion:

• Political gaslighting is increasingly becoming recognised as a key aspect of the present. The world, according to conventional wisdom, is a curtain of lies that must be lifted in order to expose the truth. However, sometimes truth itself can be hazy and amorphous, making it simple to fudge for pointless purposes.

 Q2. What do you understand by AI Bias? Discuss the causative factors and also the potential solutions?

 Paper & Topic: GS III – Science and Technology

Introduction:

Meaning of AI Bias:

• Artificial intelligence bias, also known as bias in AI, is a condition where biassed outputs are continuously produced by an AI system. This is due to incorrect assumptions that were formed throughout the machine learning process.
• an instance
• A scandal involving child welfare fraud led a substantial portion of the Dutch cabinet to resign in January 2021.
• An algorithm was used to find instances of benefit misuse. This process evaluated benefit claims and categorised them according to their likelihood of fraud.
• However, because so many applications were incorrectly accepted over the years, the tax authorities have accused over 26,000 parents of stealing child assistance.
• The parents were left with a sizable amount of debt to repay and few legal options. Ethnic minorities began to receive an excessive amount of attention as a result of the substantial additional examination that many people with dual citizenship were subjected to.

 How does this happens?

• Algorithms for machine learning are a crucial component of the technology. The idea of “training” is applied in the production of these items. The system is exposed to a large amount of data during this training phase, which is utilised as a sample to assess its judgement and predicting skills.

.Anomalies in the algorithm’s output cause bias in AI. Several factors could be at blame for this:

• Because of algorithmic issues, there is algorithmic prejudice.
• Sample bias results from issues with the training data for the machine learning model.
• As a result of false assumptions, stereotypes, prejudices, etc., the data is biassed in a prejudicial way.
• Measurement bias results from problems with the reliability, measurement, or evaluation process of the data.
• Exclusion bias results from the absence of crucial data points.

How should it be managed?

• A crucial component of the solution is inclusivity. Both the system architecture and the data collection techniques must be inclusive.
• The “black box effect” in AI systems might be addressed with more transparency. People can determine if these systems might meet their needs by thoroughly knowing how they operate.
• Given the underlying biases among those who are developing AI, it is challenging to totally eradicate prejudice, despite the fact that there are steps that can be performed. Here is an example of how a blind taste test is carried out:
• It can be used to ascertain whether particular traits, such as gender, socioeconomic level, and racial origin, have an effect on an AI system’s outcomes.
• To do this, the operation is tried both with and without the variable.
• In the interim, regulators must carefully assess, manage, and, in some situations, even stop the use of AI systems in crucial services.

 Conclusion:

• AI technology has already been implemented by other nations, and these systems have both positive and negative impacts. India is getting set to implement AI in its government. Before implementing these technologies to provide essential services, it would be in our best interests to test them for bias.