The Impact of Artificial Intelligence on Gender Bias in Corporate Governance

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BUSINESS CAPSTONE
PROJECT

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Table of Contents
Introduction................................................................................................................................2
Analysis of the Article...............................................................................................................2
Corporate Governance...........................................................................................................6
Value and Identity..................................................................................................................7
Conclusion..................................................................................................................................9
References................................................................................................................................10

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Introduction
Every aspect of the lives are now influenced and transformed by artificial intelligence
and machine learning and this has forced various big organisation to alter their long-
term vision in extent with the significant innovations and transformations (Froese and
Ziemke, 2009). Governments and companies are betting on AI due to its potential to
let computers make choices and take action in various sectors of the world such as
health care, education, entertainment, media and communications, manufacturing
and so on.
This report underpinning the inequality issue and AI skills to ensure the next
generation of workers have the skills required to create impactful change and re-
examination the policies and standards to get more women on board and accelerate
the rate of change.
Analysis of the Article
Artificial Intelligence is a tool like many technological breakthroughs focusing on
potential extreme scenarios with shaping of consumer behaviour. With innovations
such as reliable image recognition and self-driving cars on the horizon, AI is rapidly
returning to the demesne of science fiction where it goes in the public realization
(Cath et al, 2018). AI is not only shaping the customer perception while it is also
influencing decision-making and therefore, it is also very significant to acknowledge
the women role, playing in shaping the view.
Even with the vast gender gap in the tech and various other industries, and the
specific lack of diversity in the AI world, there are various women making

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breakthroughs in machine learning and AI research, and it is also important
meanwhile, AI is already going biased against women but they are working in all
aspects of AI. In addition, this leads various women to help other women in
flourishing within the space. Kakarika (2013) stated that diversity breeds success
and something as influencing and possibly far-reaching as there can be an
advantage with AI from variety of perceptions. Other than this, many are already
worried about AI learning gender bias and those with a less positive view of AI are
worried that if AI does not learn the emotional intelligence that women are more
likely to possess, AI will signify a very restricted view of the world and not be able to
associate to all of humanity.
This article is a part of the World Economic Forum Annual Meeting stating that reality
is likely to be far more ordinary as artificial intelligence is significantly being exploited
to impact on the product people purchase and their decision making, taking of hiring
decisions in the company and practice various behaviour. In previous time, the term
“garbage in, garbage out” concisely summed up the significance of high-ended data.
When the computer was given wrong data to operate with, the outcome they bring
up with is unlikely to be beneficial. However, the major issue with AI is the embedded
algorithms that come out with existing biasness. Biased AI systems are likely to
become a progressively bigger issue as AI transfers the data from science labs into
the real domain. In addition, without training on data evaluation and seeing the
potential for data biasness, susceptible groups in the public could be hurt or have
their rights intruded through biased AI. Taking an example of a recruitment process
in an organisation, an enterprise that has previously selected male applicants will
discover that female applicant was rejected by AI, as they do not fit the mould of past