As AI sweeps through business processes like a flash flood, trying to fight bias can feel like holding on for dear life. To discuss the profound impact of data bias in AI, we enlisted the vast expertise of Steve Elcock, Founder & CEO at elementsuite. In this CEO.digital exclusive guest article, Steve goes into detail about what causes bias, how it impacts business and people, and how to protect against it as we move towards a more inclusive, diverse, and fair future.
So great is the concern around the dangers of bias in AI that the UK Government recently launched a new Fairness Innovation Challenge to fund solutions tackling bias in AI systems. But what does this mean, and what can CEOs do to start addressing and eradicating bias in their organisations?
Organisations are investing heavily in AI. But data bias, and its representation in AI models can exert negative impacts on decision-making processes and business operations in many ways. It can erode customer trust, distort outcomes, and cause erroneous decisions. Ignorance of large-scale data bias is particularly alarming, given the widespread integration of technologies like machine learning (ML), AI, and other data platforms used by hundreds of millions of users worldwide.
Business leaders must act quickly to address data bias, or risk being unable to adapt to technological innovations. The crucial first step is to understand that it exists so that leaders can take measures to counter it.
What is data bias and why does it happen?
As humans, we have an innate ability to comprehend the world around us. We instinctively seek patterns in data. Since AI consumes human knowledge and mimics the brain itself, data bias in AI models is inevitable. In simple terms, AI bias is a phenomenon that occurs when an algorithm produces results that are systemically and unfairly inaccurate or prejudiced due to mistaken assumptions in the ML process. This can lead to incorrect, discriminatory, or skewed outcomes when that data is used.
Tools like ChatGPT often demonstrate selection bias wherein the probabilistic nature of language models (next letter/word prediction) may result in the sample overrepresenting or underrepresenting certain groups. For instance, if you collect data on job performance from employees who self-select participation, you may miss the perspectives of those with lower job satisfaction. Sociocultural biases may also reflect existing stereotypes or prejudices, if a database over-represents one racial group due to existing recruitment practices with unfair hiring outcomes.
Who is responsible for mitigating data bias?
Many businesses are uneducated or potentially ignorant of the importance of their data (internal to their business and externally to feed on) and the extent to which it can be used to drive fairness and competitive advantage.
Staying informed about the evolving landscape of AI, data bias and its management is essential. All business leaders in the C-suite, not just CIOs, should be aware that data bias can affect everything from optimising the supply chain in operations to diversity and fairness in HR.
Given the exponential rate of data growth, organisations will need to compress and consolidate data within and across business functions to gain competitive advantages within their business processes. However it’s also crucial that the accuracy of ground truth data is not threatened as an accepted expense of compressed data models that simplify strategic decision-making.
All business leaders in the C-suite, not just CIOs, should be aware that data bias can affect everything from optimising the supply chain in operations to diversity and fairness in HR.
STEVE ELCOCK, CEO, elementsuite
The impact of data bias on operations and decision-making
As AI and machine learning gain broad traction, the impact of data bias becomes increasingly prominent. The potential fallout from these errors is far-reaching and often poses a serious unchecked risk.
From a workforce management and employee experience perspective, these risks are many and varied. For instance, discrimination and reinforcement of stereotypes may result in unfair resource allocation or disproportionality, such as through AI face recognition which is well known within AI communities to have accuracy biases for different ethnic groups. This poses follow-on risks of loss of talent and loss of competitive advantage. There are also legal and regulatory risks, which are not yet well-formed – hence the importance of the Government’s AI Safety Summit, and the outcomes we hope will follow.
In HR specifically, hiring and recruitment can be fundamentally biased, whereby the organisation hires qualified individuals from underrepresented groups. Regarding promotion or compensation, biases in performance evaluation or promotion decisions might lead to losing high-performing individuals from within the company. It could be that more optimised training and development of individuals goes overlooked. Also, the perception and reality of bias may affect morale and engagement, particularly when considering the role of AI in workforce scheduling and equitable shift allocation.
The benefits of unbiased data
By understanding how to un-bias data, business leaders can make the best of skills in their organisations which would otherwise be overlooked due to bias. Objective decision making, and improved diversity and inclusion can lead to significant competitive advantage.
Making sure you’ve got the right people in the right place at the right time is critical. More companies are embracing automated workforce management, continually adding more data into schedule production. Weather, events, and many more external and internal factors are considered in labour forecasting, rota generation and shift allocation. Increasing data input is inevitable.
It comes down to identifying skills correctly and training staff with the least friction. Applied over a whole enterprise this becomes a big challenge for workforce managers, and using tech can make their lives easier.
What are the biggest barriers to addressing data bias?
The biggest barriers to mitigating data bias are lack of understanding or awareness of data bias and needing the right talent in the business to address it. Then there’s also the collection of data and the motivation to collect it – if leaders don’t have the right tech in place, bias can be interpreted in its collection. Even after data collection, tech teams don’t always have the experience or skills to make sure what’s collected is benchmarked, appropriate and fair.
AI models are only as good as their input. So it’s worth being cautious about implementing niche or ‘best of breed’ talent SaaS applications that only work with a narrow band of data but claim wide-ranging AI benefits. This might only reinforce existing HR biases. Only ground truth data from a full suite solution can look at all data holistically, incorporating other factors like actual employee success, absenteeism, and productivity.
If we don’t understand HR data bias and let it guide key employment decisions, it will shackle us to the mistakes of yesterday, perpetuating discrimination and undermining diversity efforts.
STEVE ELCOCK, CEO, elementsuite
Identify, address, measure and mitigate data bias
Businesses need to identify, mitigate, and prevent bias in their data and algorithms. This includes investing in diverse and representative datasets, using “fairness-aware” machine learning techniques, and ensuring transparency in their decision-making processes. Benchmarking is extremely important to ensure fair models.
To mitigate data bias, it’s crucial to do meticulous data audits and assessments before designing guidelines and policies for data collection. Data teams must use diverse and representative datasets, and employ techniques like debiasing algorithms, fairness-aware machine learning, and transparency in data collection and modelling. Additionally, ongoing monitoring and auditing of systems and data are important to detect and rectify bias when it arises. As a part of this, they must evaluate the technology and data platforms they are using.
Using a complete AI toolset as part of an employee-centric SaaS HR and Workforce Management platform offers unrivalled data recollection and HR process assistant capabilities to boost efficiency and productivity in HR operations. In addition, whistle-blower mechanisms can enable employees to safely report back on any discriminatory information, or bias identified in the workplace.
In technical terms, there are clear actions for sample re-weighting which tech teams must undertake to de-bias their system and see equal representation. The first of these is to collect ground truth data correctly – making sure you have a large and proportional data set for whatever you’re trying to deal with in the business. The second stage is to proportionalise the dataset during training to boost under-represented datasets. Ongoing testing and evaluation are also important, to continually check the continually evolving ground truth. This avoids being stuck with a model that is based on incorrect data and assumptions from several years ago.
HR’s role in addressing data bias for a thriving workplace
Above all, breadth of data is the most important element for eliminating bias. One key challenge for all organisations is to define whose role it is to own data fairness. HR may be intrinsically involved in hiring and supporting the development of the team, but might struggle to understand the HR data value chain. HR will therefore need to invest in the right data science and data engineering skills to ensure the right outcomes. Only by proactively addressing bias will CHROs achieve equity, diversity, and inclusion within the workplace.
Where their biggest asset is concerned – their people – HR must become more data-led, so that organisations can fully trust their data and make objective decisions. But data bias in HR often goes unnoticed or unchallenged because it can be masked by the illusion of objectivity that data-driven decision-making offers. HR professionals and organisations must actively address data bias by regularly auditing their data sources and algorithms, ensuring they are representative and free from historical prejudice.
If we don’t understand HR data bias and let it guide key employment decisions, it will shackle us to the mistakes of yesterday, perpetuating discrimination and undermining diversity efforts. To break this cycle, people leaders must eliminate the bias within organisational data and algorithms.
AI for HR has the power to revolutionise the workplace by eliminating bias, analysing data objectively and helping HR professionals make better-informed choices to create a fairer, more diverse, and inclusive workforce.
ABOUT THE AUTHOR
Steve Elcock, CEO and founder of elementsuite, is a visionary technology entrepreneur with over 25 years experience in HR technology. With a unique combination of a background in neuroscience and technology alongside a passion for AI, Steve’s vision to universalise enterprise HR processes is perfectly embodied by elementsuite’s all-in-one cutting-edge HR solution, which has the powerfully adaptable foundation to continually evolve and embrace the latest technological advancements.