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AI For Social Good

Written by: Salim Sheikh, Executive Contributor

Executive Contributors at Brainz Magazine are handpicked and invited to contribute because of their knowledge and valuable insight within their area of expertise.

 

Beyond the potential for economic growth, can Artificial Intelligence (AI) also be used for and have a positive impact on everyday people across society?



Overview


From a Western perspective, to achieve a positive impact, AI solutions need to adhere to ethical principles of both the European Commission – that states AI needs to be lawful, ethical and robust, to avoid causing unintended harm – and the OECD ¹ ‘Principles on AI’ – that states AI should be driving inclusive growth and sustainable development; designed so as to respect the rule of law, human rights, democratic values and diversity; transparent, so that people can understand AI outcomes; robust, safe and secure; deployed with accountability, so that organisations can be held responsible for AI systems they develop and use.


Importantly, AI should not be imagined as a solution on its own, outside of the context of its application: it must align with human intent and enhance (or amplify?) human capabilities.


Not all applications of technology aimed at delivering positive social impact have managed to achieve their goals. For AI, the result and experience must be different – for society’s sake.


Enjoy the article!


Introduction


We are witnessing a pivotal time in human history, as Artificial Intelligence (AI) is integrated into just about every new or established industry. It is the "x factor" or "secret sauce" that is helping to accelerate the Fourth Industrial Revolution (4IR). Coupled with the global COVID-19 pandemic and our collective experience of "lockdown", everyone young and old is truly experiencing noticeable changes to how we live, work and engage with one another.


With this backdrop, few would debate that AI (and emerging technologies) will transform our lives. The question is: will it be for better or for worse?


AI for business


Much of the narrative around AI is essentially about changing how companies do business.

Companies adopting AI expect to see increased productivity. New efficiencies can be derived from streamlining tasks that previously took humans weeks to complete and improving work processes by pairing people and machines in new ways.


Boards and Private Equity (P.E.) investors will expect CXOs to answer critical questions about how AI will fit into the company’s strategy along with its opportunities and risks. Consequently, leadership and management teams will be expected to answer deeper questions relating to:

  • How will AI could transform our products or services and which aspects of our business could benefit from increased automation or machine learning?

  • Have we considered the potential efficiency and productivity benefits that may come with adopting AI?

  • How might AI fit with other emerging technologies we are investing in?

  • Do we have the computing power and infrastructure to support the use of AI?

  • Do we have the digital skills and talent to move forward?

  • How will we gain the trust of our stakeholders if we use AI?

  • How can we ensure that biases do not alter AI decisions?

  • Do we have established practices and controls in place to minimise any reputational, regulatory compliance or other risks?

  • Have we thought about how we would use data collected by AI?

  • Have we considered cyber risks and data privacy issues?

To truly capitalise on AI, companies will need to consider a myriad of questions which are multidimensional affecting "people, process and technology" — all of which will be costly.


AI for society


In contrast to business and industry, fewer considerations are made publicly about the use AI for society and social good.


To be clear, this is not about showcasing the latest in AI technologies — from drones, exoskeletons, and robotics to avatars, autonomous cars, and AI-powered health solutions.


All too often, we naively empower marketing agencies, analysts and businesses to propel the belief that AI is a "silver bullet" that will help society solve key issues of the future alongside topics such as democracy, economic inequality, social welfare, and justice.


There are few platforms or institutions that are available to share successes and failures of the intersection of AI and social problems. Where they do exist, they are still in their infancy and currently only offer philosophical notions of social good. Greater focus needs to be applied to connect researchers to civil society organisations, NGOs, local governments, and other organisations to enable applied AI research for beneficial outcomes. Additionally, data created from any learnings and outcomes should be shared across countries, borders and societies perhaps via a pact with leading tech companies that link back to societal organisations that can collectively make social change happen with the right government and civil support.


On the 75th anniversary of the United Nations, one exemplar worth calling out is the UN's Sustainable Development Goals (SDGs). This is a blueprint to achieve a better and more sustainable future for all. The SDGs promise action on 17 critical social and environmental issues to address the global challenges we face, including poverty, inequality, climate change, environmental degradation, peace and justice — by 2030.


If we were to think of the SDGs as an incredibly powerful employee and citizen engagement opportunity, it would allow all businesses and people across civil society to have a sense of a shared purpose — that connect people to businesses, their communities and the world.


In the midst of a pandemic radically transforming our economies and societies – this provides a more sobering wakeup call that should make us all pause to reflect on the world as it is, as it was, and as it could be. As a human family, and a global collective, we have an opportunity to reimagine and reshape the future.


Equality versus equity



It is undeniable that AI will continue to transform every facet of our work, play, and home lives, and benefit organisations in terms of making better decisions and predicting outcomes.


The vast amounts of data sets collected and analysed by AI to predict patterns and outcomes are raising issues around privacy, security, ethics and transparency. The disruptive potential of AI poses looming risks around Fairness, Accountability, Transparency, and Ethics (F.A.T.E.). However, let's side step this for a moment and accept that there are plenty of calls to action for greater governance to avoid these negative repercussions.


Let's remain focussed on "AI for society": creating AI that supports equality, transparency, and democracy. After all, these are presumed to be the pillars and foundations required to support aif or all. However, I believe herein lies a fundamental problem: our language.


What is equality?


The Equality and Human Rights Commission describe equality as:


“Ensuring that every individual has an equal opportunity to make the most of their lives and talents.”

In other words, equality means ensuring that everyone has the same opportunities and receives the same treatment and support.


What is equity?


Equity is about giving people what they need in order to make things fair.


Giving more to those who need it


This is not the same as equality, nor is it the same as inequality. It is simply giving more to those who need it, which is proportionate to their own circumstances, in order to ensure that everyone has the same opportunities.


Equality vs. equity


The difference between equality and equity must be emphasised. Although both promote fairness, equality achieves this through treating everyone the same regardless of need, while equity achieves this through treating people differently dependent on need. However, this different treatment may be the key to reaching equality.


Since equality and equity are often used interchangeably, let’s ensure we are using the same vocabulary.


Equality → Sameness


I get two apples. You get two apples


Inequality → Unequal


I get two apples. You get no apples.


Equity → Fairness


I get two apples. You get two oranges because you are allergic to apples.


Inequity → Unjust


I get two apples. You get two apples, even though you are allergic.


Equity does not undermine equality, but rather provides the means to achieve this. Equality is undermined when equity is used incorrectly; it is undermined when a person or group’s needs are not taken into account, i.e. giving less to those who need it and more to those who do not. For example, giving women in the engineering workforce less support based on low numbers rather than high need. There are many other examples we can all share.



In a video posted to Twitter a few days before the 2020 election, now Vice President Kamala Harris wrote: “There’s a big difference between equality and equity.” In the video (with over 6.4 million views), Harris states:


“Equality suggests, ‘Oh, everyone should get the same amount.’ The problem with that: Not everybody’s starting out from the same place. So if we’re all getting the same amount, but you started out back there and I started out over here, we could get the same amount, but you’re still going to be that far back behind me… Equitable treatment means we all end up at the same place.”


Our choice of words can fundamentally change the course (and the potential) of our society.


Ultimately, there is a fine line between equity and inequality, which we must ALL be careful not to cross. Otherwise, we will only reinforce structural, cultural and economic biases.


Final thoughts


AI is rapidly developing and is increasingly being applied across sectors, posing significant ethical and societal challenges. There is, therefore a national and global need to adequately equip future leaders and decision-makers to address these challenges across business, public, and social sectors working with AI.

In the context of "AI for Society", to create true equality of opportunity, equity is needed to ensure that everyone has the same chance of getting there. We must be cautious when dealing with equity; providing too little to those who need it and too much to those who do not can further exacerbate the inequalities we see today.


We need "AI for social good" initiatives to empower employees, customers, individuals and communities across society in order to truly benefit from AI and emerging technology.



We need to continuously "zoom out" and look at society as a whole rather than focusing on minutiae siloed topics that are heavily entrenched by structural and cultural inequalities.


We need "Storytellers" and "AI experts" alike to help everyone understand how AI and emerging technology might tackle various societal challenges.


We need to think about our fellow humans... across all borders, countries, and nations.


Follow me on LinkedIn, Twitter and visit my website for more info!


 

Salim Sheikh, Executive Contributor Brainz Magazine

Over the past 25 years, Salim has built a career in consulting, working both client ‒ and supplier-side as an interim CIO/CTO and a Business Change / Transformation Consultant. facilitating digital and technology transformations programmes that have included rescue & recovery ("turnaround"), process optimisation & improvement and organisational change – across diverse industries in the UK, Europe, Nordics, Turkey, UAE, US, and Australia.


Salim is an Oxford University alumni who also has strong academic roots in Artificial Intelligence (AI). He is a mentor in the “Responsible Tech Program” managed by “All Tech Is Human” where he advocates “AI for Social Good” and “AI for All”.


He authored "Understanding the Role of Artificial Intelligence and Its Future Social Impact" which is available via IGI Global (https://bit.ly/34cfJVf) and Amazon (https://lnkd.in/gbk-zba).

 

Reference:

[1] The OECD or “Organisation for Economic Co-operation and Development” is an intergovernmental organisation with 38 member countries which was founded in 1961 to stimulate economic progress and world trade. (Source: Wikipedia)

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