How can artificial intelligence help to create a more inclusive labour market?
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How can artificial intelligence help to create a more inclusive labour market?
When applying for work, everyone should experience a fair recruitment process. Unfortunately, this is not always the case, as human biases can lead to unequal opportunities for different individuals and groups. At the University of Alberta in Canada, Professor Linglong Kong and Professor Bei Jiang are investigating the role of artificial intelligence (AI) in labour market processes, and developing new AI algorithms that are free from bias and promote equality.
Talk like an artificial intelligence researcher
Algorithm — a set of rules or instructions that can be used to perform tasks or solve problems
Artificial intelligence (AI) — machines or computer systems able to perform tasks using skills that are typically associated with humans, such as reasoning, perception, learning and problem-solving
Bias — ways of thinking that favour or oppose individuals, groups or things
Data mining — analysing large datasets to discover patterns or learn useful information
Labour market — the supply and demand of workers, involving employers and employees
Machine learning — the development and use of algorithms to analyse and understand data without the need for explicit instructions
Screening — the process of sorting through job candidates to determine if they have the necessary skills and experience for a role
Socioeconomic — relating to the relationship between economic status and social factors
Imagine that you have to choose people to work with on a group project. Who would you pick? Would you choose the people with the most relevant skills? Or, are you more likely to pick your friends and people who you are more familiar with, even if you know they might not have the relevant skills? Although we might recognise that picking people with the right skills is more important, in reality, we are likely to be influenced by our own biases. These same biases exist in labour markets, where they are often the source of inequality and discrimination.
“Bias exists in many aspects of labour markets and profoundly influences hiring processes, job advertisements and professional networking,” says Professor Linglong Kong from the University of Alberta. Linglong and his colleague Professor Bei Jiang are examining ways of removing this bias using artificial intelligence (AI).
Where does bias exist in labour markets?
Applying for a job should be a fair and equal process based on how a candidate’s skills and experiences make them suitable for the position. However, much of the recruitment process is influenced by human biases. These biases can influence the language used in job advertisements and carry on through initial screening, interviews and the final selection of candidates.
“Human decision-making is prone to bias, and, throughout the hiring process, recruiters may unintentionally give preference to applicants with similar histories or traits to themselves,” says Bei. “This may result in prejudice against deserving people from other backgrounds who don’t match the stereotypically accepted profiles.” These biases and prejudices can also take place in professional networking. “Personal relationships and recommendations may favour or exclude underrepresented groups and sustain unequal access to opportunities,” continues Bei.
What role does AI currently play in the labour market?
AI is used in labour markets to streamline the recruitment process by supporting tasks such as résumé screening and candidate selection. However, AI algorithms are trained on data from past recruitment processes, which are likely to have been skewed by human biases. As a result, these algorithms unintentionally perpetuate the original biases. “AI algorithms are filled with biases that reflect historical preferences towards specific educational backgrounds or demographic characteristics (such as gender and race),” says Linglong.
Similarly, the language and placement of AI-supported job adverts can be impacted. “Language choices used by AI may appeal to and target a certain gender or ethnicity group over another,” explains Linglong. “Moreover, adverts may be targeted towards certain platforms or networks that are more commonly used by specific demographics, limiting the diversity within applicant pools.”
On professional networking platforms like LinkedIn, users are given recommendations of people to connect with and job opportunities to apply for. “Again, the AI algorithms used on these platforms are trained on biased data. This results in the reinforcement of existing professional networks and poses a disadvantage to underrepresented groups,” says Linglong.
How are Linglong and Bei studying biases in AI and labour markets?
Linglong and Bei use data mining techniques to uncover the biases that affect AI decision-making. They also use surveys and interviews to understand how people involved in labour market processes are influenced by bias or affected by biased decisions.
Their research has already yielded some insights. “Our research demonstrates that biases in AI algorithms not only affect individual outcomes but also have a systemic impact on employment and career opportunities,” says Bei. In other words, the problems caused by these biases are not limited to individual cases of discrimination and missed opportunity. Instead, they permeate through entire labour markets, stalling growth and increasing inequality. “With the help of our study, we hope to create policies that lessen bias and promote responsible Al practices that enhance fairness and inclusivity in the labour market,” says Bei.
How can responsible AI be created?
“We are creating and applying cutting-edge debiasing methods that are specifically designed for AI algorithms used in hiring processes, job advertisements and professional networking,” explains Linglong. These cutting-edge methods are helping Linglong and Bei create AI algorithms that are more sensitive to gender, ethnicity and other biases. “Thus far, we have achieved notable progress in preserving Al performance while effectively reducing bias,” says Linglong. “With our debiasing techniques, we have demonstrated that it is possible to achieve fairer outcomes when it comes to hiring decisions and job advert placements.”
What impact would responsible AI have on labour markets?
Reference
https://doi.org/10.33424/FUTURUM552
“Responsible AI can enable employers to make recruitment decisions based on matching a candidate’s qualifications with the job requirements, independent of demographic factors,” explains Bei. This means that workforces can become more diverse, and individuals from a wider range of backgrounds will be given the opportunity to succeed and develop. “Practically speaking, responsible AI makes sure that networking opportunities are suggested impartially and employment adverts are targeted inclusively, increasing opportunities for people from all walks of life,” enthuses Bei.
Professor Linglong Kong
Professor, Department of Mathematical and Statistical Sciences, University of Alberta, Canada
Canada Research Chair in Statistical Learning
Canada CIFAR AI Chair, Fellow
Alberta Machine Intelligence Institute (Amii)
Professor Bei Jiang
Associate Professor, Department of Mathematical and Statistical Sciences, University of Alberta, Canada
Canada CIFAR AI Chair, Fellow
Alberta Machine Intelligence Institute (Amii)
Fields of research: Statistical machine learning, artificial intelligence
Research project: Developing bias-free AI algorithms that will promote equality in labour markets
Funders: Social Sciences and Humanities Research Council (SSHRC) under the scheme of the Canada-UK Artificial Intelligence Initiative, Economic and Social Research Council (ESRC)
About artificial intelligence
Artificial intelligence (AI) research involves the study and development of systems and algorithms which enable machines to complete tasks that could previously only be done by humans. It is a multidisciplinary field, with contributions from computer science, social science, mathematics, neuroscience, linguistics and philosophy. As well as using techniques such as machine learning and robotics, AI researchers need to think about potential ethical and societal issues, such as bias, accessibility and how AI could impact on employment.
Much AI research focuses on creating systems that can adapt, improve, learn and work on their own. These systems often simulate human abilities such as reasoning, perception, language and problem-solving. Recently, AI has been incorporated into products including self-driving cars, chatbots (such as ChatGPT) and robots that can assist with surgery in healthcare settings.
“Technology changes at a rapid pace, so we are always learning,” says Linglong. Working in such a fast-paced field comes with challenges, including the technical challenges involved in creating reliable AI systems and the ethical issues of data privacy and bias. Bei, Linglong and their colleagues at the University of Alberta are part of the Alberta Machine Intelligence Institute (Amii) which builds collaborative relationships between industry and those working in academia. “It will require cooperation, diverse viewpoints and adhering to moral and ethical standards to overcome these challenges,” continues Linglong.
Despite the challenges, there are many benefits to working in AI research. “Contributing to cutting-edge research in AI allows us to influence many international industries including healthcare, finance and transportation,” explains Bei. “The opportunity to work on ground-breaking projects, attractive pay packages and the satisfaction of using technology to make a meaningful contribution to society’s advancement serve as some of the benefits of working in AI.”
Pathway from school to artificial intelligence
Subjects including calculus, linear algebra, statistics, computer science and data science will provide many of the skills and much of the knowledge needed for working with AI.
Becoming familiar with programming languages such as Python and Java will also be useful, as will subjects such as social sciences and ethics, which will help you to understand some of the issues relevant to the development and use of AI.
Amii has educational programmes that include workshops, seminars and summer camps which introduce students to AI concepts and applications. “These programmes grant students access to the university’s world-class AI research facilities and provide hands-on learning experiences,” says Linglong.
“Gaining practical experience and developing a solid portfolio can be accomplished by taking part in AI competitions, joining AI societies and online forums, and pursuing research projects or internships,” says Bei.
Explore careers in artificial intelligence
“Job roles in AI include machine learning engineers, AI researchers, robotics engineers and AI ethics consultants,” says Linglong. “These positions offer opportunities to innovate in a number of fields and use technology to propel society forward.”
Read this blog post by Tom Silver, an AI researcher who shares his advice on starting out in this career.
AI organisations exist in many countries around the world. They are great places to stay up to date with the latest developments in AI research. The Association for the Advancement of Artificial Intelligence has a list of some of the major societies.
Contact AI researchers and scientists and ask them questions, both about their research and their career progression.
Meet Linglong
As a child, I was drawn to puzzles and problem-solving which naturally evolved into a fascination with statistics and probabilities. My undergraduate studies in probability and statistics solidified this interest, steering me towards a career that blends statistical theory with practical applications.
My technical proficiency in statistical machine learning, honed during my PhD at the University of Alberta, has been instrumental in my career. Characteristics such as resilience and a methodical approach to research have allowed me to navigate complex datasets and scenarios, particularly in neuroimaging data analysis.
My motivation has always been driven by the challenge and potential impact of my work. It is rewarding to apply complex statistical models and machine learning techniques to critical issues in healthcare, such as precision medicine and dynamic treatment regimes. It is immensely gratifying to contribute to advancements that may directly improve patient outcomes and lead to more tailored, effective treatments.
To maintain balance, I engage in hobbies that are completely unrelated to my professional interests. I find solace in nature, often hiking or bird-watching, which helps me disconnect and return to my work refreshed and inspired. I also enjoy practicing calligraphy, which allows me to focus on the moment and hone my patience and attention to detail.
Linglong’s top tips
1. Develop a solid foundation in core mathematical concepts, as they are crucial across all STEM fields.
2. Embrace collaboration, as interdisciplinary approaches often lead to groundbreaking innovations.
3. Develop a resilience to failure, as it is often through setbacks that we gain our most valuable insights.
Meet Bei
Growing up, I was always intrigued by numbers and their patterns, which drew me to the field of computing and mathematics. This fascination laid the groundwork for my later studies and steered me towards a career in biostatistics. My early exposure to computing and mathematical concepts solidified my passion for data and its power in scientific research, ultimately shaping my career path.
The most rewarding aspect of my work is the ability to contribute to significant advances in healthcare through the development of statistical models. It is fulfilling to see how these statistical models can directly impact patient care and outcomes by providing more nuanced insights into the progression of diseases and the effectiveness of treatments.
A continuous drive to contribute to meaningful scientific discoveries has motivated me throughout my career. Each stage, from academic training to professional research, presented opportunities to solve real-world problems through innovative statistical methods. The potential to make a difference in public health by enhancing data analysis techniques continues to inspire and propel me forward in my career.
To unwind from work, I enjoy activities that allow me to disconnect from my daily routine and rejuvenate mentally. This includes reading non-technical literature, practicing meditation and taking photographs. These activities provide me with a fresh perspective and renewed energy, enhancing both my personal well-being and professional productivity.
Bei’s top tip
1. Engage in continuous learning and stay updated with the latest technological advancements.
2. Seek out mentoring and networking opportunities; learning from experienced professionals can provide invaluable insights and guidance.
3. Don’t be afraid to tackle challenging problems – they often lead to the most rewarding outcomes.
Do you have a question for Linglong and Bei?
Write it in the comments box below and Linglong and Bei will get back to you. (Remember, researchers are very busy people, so you may have to wait a few days.)
Learn more about how artificial intelligence can help humans:
www.futurumcareers.com/can-artificial-intelligence-detect-hidden-heart-attacks
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