Ethical AI implementation in Digital Transformation strategies
Global industries and governments increasingly rely on the automated decision-making abilities of algorithms which keep our digitally-powered world running. Artificial Intelligence (AI) supports a growing number of vital processes that allow our society to operate, from disease diagnosis and dynamic pricing to job hiring and judicial processes.
The potential for significant societal impact posed by AI implementation across the corporate, government, and private sectors is large.
Given that algorithm-powered decisions are shaping our modern world, what are the ethical implications for macro and micro digital transformation strategies?
Key ethical concerns of AI implementation
For all its evolving benefits, AI still remains a series of 0s and 1s which lacks the ability for empathy and the cognition that make up our decision-making processes. This leads to a number of ethical concerns with AI implementation.
Bias and discrimination
AI’s inherent lack of ethical structure means that it functions precisely how it is designed to – meaning those who create it define its moral and ethical compass. Despite AI being a logic-based tool, it is built by humans who inadvertently embed their own bias into the code.
For example, engineers at Amazon trained their recruitment software with resumes from mostly male programmers, so when it came time to screen candidates, any resume that listed female elements was automatically disqualified by the system.
There have been numerous other examples where human bias has infiltrated AI programs and resulted in either sexist, ageist or racist returns when it comes to the system’s recognition or predictive capabilities.
Privacy and data security
To provide tailored experiences, companies need to gather as much user data as possible to understand each customer’s preferences based on their online interactions. At the same time, customers expect businesses to safeguard their personal data and respect their online privacy.
The challenge of balancing personalisation and privacy in the digital age is a big one. Europe’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), together with growing public concerns around general data collection, have left organisations with no choice but to leverage the power of AI to ensure their user data is protected and compliant. But handling sensitive user data using AI is an ethical dilemma that needs to be addressed.
Accountability and transparency
Concerns over how big data is collected and used, and issues surrounding transparency within AI development and rollout have gained the attention of governments. The EU, USA, and China are now implementing their own versions of AI regulation with the aim of ensuring that AI and similar technologies benefit society rather than impede human rights.
As private companies are generally leading the development of AI technology, there are concerns regarding the implications of unelected agents gatekeeping global access and usage of this technology.
Impact on employment
Algorithm-centric business approaches potentially exacerbate economic inequality due to a ‘code ceiling’ that prevents upward mobility and career advancement. When daily tasks are managed by an algorithmic feedback loop, it creates systemic inequality due to the hierarchy of information flow.
This reduces the ability of workers to gain upward mobility within an organisation as they are only fed information pertaining to their specific tasks.
Best practices for addressing ethical challenges
Identifying the key areas of ethical concerns regarding AI implementation is the first step. The second is to take appropriate action to address them.
Businesses can ensure ethical digital transformation by developing their own AI ethics guidelines and policies. While this can be specific to the company, there are a number of standardised fundamental approaches that can be applied to ensure an ethical approach is considered when undergoing digital transformation.
Ensuring diverse and inclusive data sets are used across an entire business ecosystem is another good way to eradicate discriminatory business processes. This means training IT teams to source balanced data to integrate into business systems and identify problematic information to flag and remove.
Businesses must also implement privacy-preserving techniques that are designed to protect the sensitive data of customers. Automated privacy policy enforcement, anomaly and intrusion detection, and privacy-preserving machine learning (PPML) are a few techniques that companies can leverage to ensure AI systems and tools remain ethically compliant.
There is a growing push toward encouraging transparency in AI algorithm development. Numerous forums and bodies, including governments, are realising the importance of transparent and ethical AI development to ensure safety for all. Businesses can apply these regulatory approaches to their own AI implementation to remain compliant and ethical.
Initiatives in the field of AI Ethics
Fortunately, there is a growing number of institutes, research centres, and initiatives that are designed to promote the ethical development and usage of AI.
As more businesses and governments rely on algorithms, decisions influenced by AI thinking and worldviews will increasingly affect people’s daily lives. This opens the door to machine learning (ML)-based AI systems trained with incomplete or distorted data to result in a warped “worldview”.
This can magnify prejudice and inequality, spread rumours and fake news, and even cause physical harm. As society continues to adopt more AI/ML services, the urgent need for a centralised approach to ethical AI development has never been more imperative.
Practices such as Human-centric Artificial Intelligence (HAI) promote the transparent and ethical development of AI to ensure it works for society, not against it. The Institute for Ethical AI and Machine Learning is another research centre dedicated to developing frameworks that support the responsible development, deployment and operation of machine learning systems.
There is a clear and decisive approach to global ethical AI development and implementation that values transparency and morality. There are government and private sector players that are growing in influence who identify the need for sensible regulations of AI adoption and usage to ensure a safe and ethical digital society.
Conclusion
Now that algorithms are making decisions which directly impact the lives of humans, it has become imperative to address the ethical dilemmas embedded in this ecosystem.
Globally, there is a lack of clarity or consensus around the meaning of central ethical concepts and how they apply in specific situations. Insufficient attention has been given to the tensions between ideals and values. Additionally, there is not enough understanding of both the technological capabilities and their potential impacts, as well as the perspective of the general public on the development of these solutions.
To create truly inclusive AI, the core ethical elements need to be addressed and a consensus needs to be reached between all governance structures.
Mario Grunitz
Working Machines
An executive’s guide to AI and Intelligent Automation. Working Machines takes a look at how the renewed vigour for the development of Artificial Intelligence and Intelligent Automation technology has begun to change how businesses operate.
