Management 1: Business Capstone Project on AI and Gender Bias

This report, a business capstone project, investigates the pervasive issue of gender bias within artificial intelligence and its subsequent impact on corporate governance. The analysis begins by examining how AI, influenced by biased data and algorithms, can perpetuate gender inequality, particularly in areas like recruitment and job opportunities. The report highlights the importance of addressing these biases to ensure fairness and promote diversity in the tech industry. It delves into the role of corporate governance in mitigating these biases, emphasizing the need for data evaluation, algorithmic testing, and the establishment of standards to safeguard against discriminatory outcomes. Furthermore, the report explores the interplay between individual identity, societal values, and how these are shaped by AI. It concludes by emphasizing the need for proactive measures, including industry cooperation and the development of robust standards, to ensure that AI systems do not exacerbate existing gender gaps but instead contribute to a more equitable and inclusive future. The report references several sources to support its findings, highlighting the evolving challenges and opportunities presented by AI in the business world.

Running Head: MANAGEMENT 0
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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

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successful candidates. This represents the gender biasness in the organisation
irrespective of candidate potential and ability.
There were digitalization in the western world from a longer period of time and thus
there are more data and information for AI to construe (Zhou, 2013). With regards to
this, at various sides and stages, women have been overlooked and therefore to fill
up AI with quality information in relation with women, there is less or nil availability.
It can be said that algorithms never think for themselves. If it had opinions and
personhood of its own, it might stand up to; those who make it learn instances
dripping with prejudice. Hence, there is only a need for building adequate standards
and ensure it testing to full potential so that to discover relevant biasness and do
make it correct with new algorithms (Cairns, 2019). Not doing this will result in
generating larger breaks in gender gaps and AI skills. For instance, Industry AI
gender gaps are presented in the below picture –
Source: (Cairns, 2019)

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While this is exciting and important work, the potential for bias to overturn drives for
fairness and equality runs deeper, to points, which may not be so easy to repair with
algorithms. These imbalances can be addressed by embracing a greater focus on
equality, inclusion and empowerment. In technology sector, it has been seen that
more women’s are working with adding efforts into development of products and
writing algorithms. However, there are various challenges for them as male has
dominated engineering and technology domain over a long period. Hence, this
unconscious bias is increasing and in many organizations, women’s are fostering
performance with their creative ideas and helping the enterprises at time of crises.
According to a recent review by American Association of University Women, only 26
per cent of computer professionals were women in 2013 and that figure has dropped
to 9 per cent since 1990 (Furness, 2016). It was also found that AIis already biased
as last year as per the study, fewer women than men were shown Google
advertisement for high paying jobs. Many researchers build a tool named as
AdFisher that forms simulated profiles and check browser experiments by visiting the
internet and gathering data on how minor changes in profiles and preferences will
impact the content shown.
“In comparison with the female, the male users were shown the high paying jobs ads
about 1,800 times and the females saw those ads about 300 times” (Furness, 2016).
Many big enterprises have undertaken various approaches while introducing girls to
the realm of technological domain and help them to go for additional education in the
STEM subjects and this step their footprint into the male dominated arenas.
However, this progress is very slow, as it is not getting full support in relation with
top-down change. Hence, it is important to bring out change so as to present various

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opportunities to the next generation of women and reduce the biased algorithms of
technology and AI. Women’s will surely able to create an impactful change with the
necessary skills and AI. However, to achieve this, there is a need for setting
standards and industry level cooperation so that to gives out quality data and thus
there will be a far greater chance to spot and halt, bias in data, algorithms or
systems before it is continued and turn into unsafe.
Corporate Governance
Artificial Intelligence is creeping its way and currently influencing the way by
transforming many areas in the business world (Bilal et al, 2016). It was founded that
external spending on AI-related projects went gone up to $12 billion in 2016
(Makridakis, 2017). Artificial intelligence can also play an important role in corporate
governance. For example, it can create big decisions from data driven knowledge
and streamline decision-making process with predicting the future outcome of that
specific decision.
The data fetched up related to biasness of women or their role from AI technology
functions well as a convincing tool for the other board members of an organisation or
executives. One of the biggest improvements in corporate governance mechanism
comes from data evaluation. Many executives and board members are concerned
over automated systems to attain high-level leadership task. However, the true
advantage of artificial intelligence in corporate governance is the ability to collect and
analyze data. Corporate governance can gain a lot from the execution of an AI
solution while exploited as an augmentation for high level talent (Spohrer and
Maglio, 2008). In many countries, various initiatives are taken out for the
development of AI and in near future, it will also develop many employment

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opportunities. It is also clear that AI in corporate governance is not only transforming
the way boards undertake decision but also gives various insights deeply rooted in
an organisation structure.
There is also a link between social biasness and corporate governance, which cause
negative effects of increased female representation on firm performance (Cooke,
2011). It was found that individuals are likely to recognize others and themselves in
terms of salient social categories and one of the extension is gender and therefore
developing various out-groups. This not increase the probability of conflicts but also
impacts on the performance of the firm. It was noted that representation on corporate
brands is either positively or negatively depending on organisation financial
performance, however, the magnitude of such a relationship is likely to be small
(Clarke and Dean, 2007). Hence, to reduce biasness, it is important for the
organisation to leverage each step in corporate governance for future proofing of the
organisation and thus taking the next step forward.
Value and Identity
The general identity-values of a society may be fully entrenched and in the current
proliferation of self-process and self-efficacy, there is a need for new conception that
can hold a coherence on the part of individuals (Hawkes and Buse, 2013). Predictive
technology nowadays such as AI, allow better exploration of the indicators that
trigger and influence human behaviour while mapping out human activities in extent
with operational responses. There are significant benefits to be gained from these
efforts. With the technologies and capabilities shift, individual identity and value will

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also be altered and at the same time people are going to recognize its inherent place
in the field of AI.
The AI age will force us to re-evaluate what an individual know and believe and how
they relate to the world and other individuals through work. In addition, the fourth
industrial revolution will change the meaning of work while changing of human
identities too. Many historical data sets reflect traditional values, which may be not fit
in the current system and thus create various barriers in the achievement of specific
goal (Kaidonis, Stoianoff and Andrew, 2010). Identity relates to basic values that
dictate the choice an individual make and organisation needs to explore these
identities to reduce gender biasness. In future, these values will be embedded in AI
in the form of certain algorithms and this impact one’s roles and responsibilities.

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Conclusion
In the limelight of above discussion, biased AI systems are likely to become
widespread problem as the data is characterized by AI into the real world with putting
of intelligent algorithms and software in various areas. In addition, there is also a
danger as without proper data evaluation and standard measures, one can spot the
potential for bias in data, that may impact rights of individuals.
While bringing successful consistency and fundamental in each variable, necessary
standards and boundaries can be set that, can help in reducing biasness to a greater
extent and make involve women in the decision making or feeding algorithms in AI.

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References
Bilal, M., Oyedele, L.O., Qadir, J., Munir, K., Ajayi, S.O., Akinade, O.O., Owolabi,
H.A., Alaka, H.A. and Pasha, M., 2016. Big Data in the construction industry: A
review of present status, opportunities, and future trends. Advanced engineering
informatics, 30(3), pp.500-521.
Cairns, A. (2019) Why AI is failing the next generation of women [ONLINE] Available
from: https://www.weforum.org/agenda/2019/01/ai-artificial-intelligence-failing-next-
generation-women-bias/ [Accessed 27/06/2019].
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M. and Floridi, L. (2018) Artificial
intelligence and the ‘good society’: the US, EU, and UK approach. Science and
engineering ethics, 24(2), pp.505-528.
Kaidonis, MA, Stoianoff, N P, and Andrew J. (2010) “The Shifting Meaning of
Sustainability”, in Aras, G and Crowther, D (eds) in A Handbook of Corporate
Governance and Social Responsibility, Gower Publication ISBN 978-0-566-08817-9
Cooke, F. 2011. "Social responsibility, sustainability and diversity of human
resources", in Harazing, Anne-Wil and Pinnington, Ashly H., International human
resource management, (third edition), London, Sage, pp. 583-619
Froese, T. and Ziemke, T., 2009. Enactive artificial intelligence: Investigating the
systemic organization of life and mind. Artificial Intelligence, 173(3-4), pp.466-500.
Furness, D. (2016) Will AI built by a ‘sea of dudes’ understand women? AI’s
inclusivity problem [ONLINE] Available from: https://www.digitaltrends.com/cool-
tech/women-in-artificial-intelligence/ [Accessed 27/06/2019].

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Clarke, F. and Dean, G. (2007) “Chapter 3: Governance Overload: A contestable
strategy”, in Clarke, F. & Dean, G. Indecent Disclosures: Gilding the Corporate Lily,
Cambridge University Press, Melbourne, pp.51-64.
Hawkes, S. and Buse, K., 2013. Gender and global health: evidence, policy, and
inconvenient truths. The Lancet, 381(9879), pp.1783-1787.
Kakarika, M., 2013. Staffing an entrepreneurial team: diversity breeds
success. Journal of Business Strategy, 34(4), pp.31-38.
Makridakis, S., 2017. The forthcoming Artificial Intelligence (AI) revolution: Its impact
on society and firms. Futures, 90(1), pp.46-60.
Spohrer, J. and Maglio, P.P., 2008. The emergence of service science: Toward
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Zhou, J., 2013. Digitalization and intelligentization of manufacturing
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