Political aspect: Political democracy requires "government by consent and political equality." Democracy, as a form of government, implies that elections must be held with reasonable frequency. Moreover, there should be more than one political party competing for political power Social Aspects: A democratic society is one in which an atmosphere of equality prevails. There should be no discrimination on grounds of religion, race, caste or sex. Every one should have equal access to shops, restaurants, hotels and places of public entertainment Our Constitution guarantees equality to every person before law. Economic aspects.: Political democracy will be a reality only when it is supported by economic democracy. The most stable democracies of the world are those which have extensive welfare schemes for the poor people. 1. What is democracy? 2 What are the various aspects of the democracy? 3. Write a few ser tenses on the social aspects in a democracy 4. Write a sentence on economic aspects.
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Answer:
Fairness is a highly prized human value. Societies in which individuals can flourish need to be held together by practices and institutions that are regarded as fair. What it means to be fair has been much debated throughout history, rarely more so than in recent months. Issues such as the global Black Lives Matter movement, the “levelling up” of regional inequalities within the UK, and the many complex questions of fairness raised by the COVID-19 pandemic have kept fairness and equality at the centre of public debate.
Inequality and unfairness have complex causes, but bias in the decisions that organisations make about individuals is often a key aspect. The impact of efforts to address unfair bias in decision-making have often either gone unmeasured or have been painfully slow to take effect. However, decision-making is currently going through a period of change. Use of data and automation has existed in some sectors for many years, but it is currently expanding rapidly due to an explosion in the volumes of available data, and the increasing sophistication and accessibility of machine learning algorithms. Data gives us a powerful weapon to see where bias is occurring and measure whether our efforts to combat it are effective; if an organisation has hard data about differences in how it treats people, it can build insight into what is driving those differences, and seek to address them.
However, data can also make things worse. New forms of decision-making have surfaced numerous examples where algorithms have entrenched or amplified historic biases; or even created new forms of bias or unfairness. Active steps to anticipate risks and measure outcomes are required to avoid this.
Concern about algorithmic bias was the starting point for this policy review. When we began the work this was an issue of concern to a growing, but relatively small, number of people. As we publish this report, the issue has exploded into mainstream attention in the context of exam results, with a strong narrative that algorithms are inherently problematic. This highlights the urgent need for the world to do better in using algorithms in the right way: to promote fairness, not undermine it. Algorithms, like all technology, should work for people, and not against them.
This is true in all sectors, but especially key in the public sector. When the state is making life-affecting decisions about individuals, that individual often can’t go elsewhere. Society may reasonably conclude that justice requires decision-making processes to be designed so that human judgement can intervene where needed to achieve fair and reasonable outcomes for each person, informed by individual evidence.
As our work has progressed it has become clear that we cannot separate the question of algorithmic bias from the question of biased decision-making more broadly. The approach we take to tackling biased algorithms in recruitment, for example, must form part of, and be consistent with, the way we understand and tackle discrimination in recruitment more generally.
A core theme of this report is that we now have the opportunity to adopt a more rigorous and proactive approach to identifying and mitigating bias in key areas of life, such as policing, social services, finance and recruitment. Good use of data can enable organisations to shine a light on existing practices and identify what is driving bias. There is an ethical obligation to act wherever there is a risk that bias is causing harm and instead make fairer, better choices.
The risk is growing as algorithms, and the datasets that feed them, become increasingly complex. Organisations often find it challenging to build the skills and capacity to understand bias, or to determine the most appropriate means of addressing it in a data-driven world. A cohort of people is needed with the skills to navigate between the analytical techniques that expose bias and the ethical and legal considerations that inform best responses. Some organisations may be able to create this internally, others will want to be able to call on external experts to advise them. Senior decision-makers in organisations need to engage with understanding the trade-offs inherent in introducing an algorithm. They should expect and demand sufficient explainability of how an algorithm works so that they can make informed decisions on how to balance risks and opportunities as they deploy it into a decision-making process.
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