Algorithm Bias




Let’s think for a moment about how we navigate through our daily routine, from the time our feet hit the floor in the morning until our head hits the pillow at night and even as we sleep, many digital devices are collecting, analyzing and storing your personal data.  We are utilizing artificial intelligence (AI) for school assignments, work emails, health tracking, and social interactions.   
Think about all those times a streaming network suggested a movie or TV series.   A topic that you searched on Google has morphed into a series of ads in your social media feed.  Those recommendations/ads are based on algorithms that examine your digital environment; what you’ve searched, watched or bought.  Artificial intelligence is about designing intelligent software that can analyze and assess the digital environment and make intelligent choices for online learning.  Artificial intelligence lies behind these algorithmic data sets.  Algorithmic data sets can be biased, which can result in a loop of cultural, social, and economic unfairness.  AI is increasingly used in education, training, and learning.  AI ought to reflect the diversity of all its users. It should level the playing field for students and workers, especially as schools and businesses around the world embrace differences, fresh perspectives, and non-traditional skillsets. Human interactions with AI should be safe, secure, valuable and useful to everyone.  Why isn’t it?  

Keywords:  Artificial intelligence, technochauvism, algorithm bias, adult education

Technochauvism
The mainstream belief about the role of technology in society is greatly influenced by utopian visions of small homogenous groups of people coding perfect algorithms and developing inclusive artificial intelligence.  We have in a way been programmed by Silicon Valley to accept that the technological solution is always the better solution, or “technochauvinism” (Broussard, 2018).  For a real-world example of “technochauvism” we can take a look at Twitter.  Twitter feels it is better to use an algorithm to push conversation snippets from other users to your Twitter feed.  This algorithm uses data information about what you post and then pushes what it “thinks” you are interested in your recommendations.  We are led to believe that technology is neutral, and the results are objective.  It is not and because of Twitter’s current algorithm conversation feed tends to trend on negative buzzwords (Broussard, 2018). 
            Wilson’s (2018) literature review of Meredith Broussard’s book, “Artificial Unintelligence. How Computers Misunderstand the World.”, he discusses the relevance of having a human connection in tandem with the development and implantation of algorithms and the uses of algorithms in AI.  Wilson (2018) concurs Broussard’s argument that the gap between what we imagine technology in the classroom or work environment can do and what technology such as computers or mobile devices can do is vast (Broussard, 2018).   Twitter can hire a community manager who can use technological tools to help improve the conversation, in turn, creating a more inclusive and global user experience.  Technology is advancing at a rapid pace and technology companies are still trying to lessen the cultural divide.  Everyday technology is being developed and the human element is being left out of the equation creating an “in the loop” machine learning where automated systems are excluding students, social groups and communities based on personal demographics that don’t meet algorithm parameters. 

Algorithm Bias

             “Discrimination is an increasing concern when we use algorithms and it really does not matter if the algorithm intentionally or unintentionally engages in discrimination: the outcome on the people who are affected is the same” (Datta, Tschantz & Datta, 2015 as cited in Jackson, 2018).   Jackson (2018) explores the various ways in which algorithms fuel biased profiling among venerable populations, thereby reinforcing rather than overriding existing biases. 
            How algorithms are designed, developed and their deployment in data capture and analysis can impact people’s lives in concealed and subtle ways that have a significant impact on their home, work and family life.   Jackson (2018) cites Amazon and its Prime shipping service.  Amazon utilized a data set of neighborhoods based on income and zip code.  Amazon was accused of “prime-lining” because the algorithm led to excluding services to low-income minority neighborhoods.  “low-income” turned out to be a proxy for race.  This is an example of unintentional bias, of how easy it is to engage in bias behavior, even when the bias is initially excluded.    Jackson also cites an example of intentional or by design algorithmic bias, a credit card company lowered a man’s credit limit because an algorithm profiled the businessman frequented stores in predominantly African American with poor repayment history.  The algorithm used his purchasing information to profile and predict what it thought to be an unbiased representation of his financial habits. 
            Algorithms are a set of unambiguous specifications or rules for performing calculations, data processing, automated reasoning, and other tasks that reduce decision making to a number (Jackson, 2018).  A number turns into a data set and this data set is then analyzed by a computer program and rendered into a repository. Repositories of data allow those who control the data to explore common patterns that emerge and be used to identify behavioral traits common among certain groups of people but not of others (Jackson, 2018).   Algorithms use data to create and infer meaning, embedding patterns in software used in AI, mobile devices and social media.  Once algorithms are embedded, they grow and spread by pulling current data and combining it with old data it creates new data, and as it does the unintentional/intentional bias develops, like a virus, everyone or anyone can be affected. 

Artificial Intelligence & Society
            Algorithms are increasingly being used to make sensitive decisions, for instance, algorithms are being used to calculate and assess which completed applications for employment move to the next step in the hiring process.  Simple errors in data entry can disqualify a well-qualified applicant from obtaining work.   Algorithms are also being used to decide which individuals receive loans basis of zip codes or who should receive bail based on the neighborhood they are returning to once released (Reynolds, 2017).  As research clearly shows the history of algorithms and AI containing hidden biases, which begs the question, “are we presenting a level playing field for everyone?” (Yanisky-Ravid & Hallisey, 2019).  Yanisky-Ravid & Hallisey (2019) purpose of a new AI Data Transparency Model that focuses on disclosure of data rather than focusing on the initial software program and programmers.
Artificial intelligence (AI) can to a great tool.  The benefits of artificial intelligence come from its ability to evaluate, learn and adopt a dynamic strategy to create an immersive experience.  Yanisky-Ravid & Hallisey (2019) argues that algorithm development needs transparency and a framework to identify and eliminate algorithmic bias.  Programmers are attempting to remove the bias from AI, but without proper training and a diverse team can inadvertently interject their own cognitive biases.  Currently, Yanisky-Ravid & Hallisey (2019), contend we need to strive to identify the risks of faulty data by hiring data managers to conduct critical audits of data used to train AI systems. 

Discussion & Conclusion
The AI discussed in the reviewed articles is the same AI that impacts the education field with mechanisms of individualized learning applications.  AI can be brought into both the traditional and distance classroom with the implementation of simulators, tutorial programs, interactive games.   The AI systems are developed to adapt to students’ diverse needs to create personalized education leading educators to rethink the teaching-learning process since the automated assistance in relation to the student help allow a new and attractive perceptive as the AI parameters facilitate the learning process (Ocaña-Fernández, Valenzuela-Fernández, & Garro-Aburto, 2019).  Here inlays the issue, the enormous mass of global citizens are in unprivileged position with respect to AI technologies and many ethnic groups who do have access to AI are finding algorithm biases within face-recognition software which in some AI programs embedded in augmented reality did not recognize dark skin pigment (Buell, 2018).   These issues make it clear the associated problems that can arise when technology is developed in a bubble without regard for how diverse the world is, we need to focus on getting individuals to think twice about what’s going on in the rapidly advancing brains that power artificial intelligence before it’s too late (Buell, 2018).    
It is time we start thinking about constructing platforms that can identify bias by not only collecting people’s experiences but also auditing existing software (Wilson, 2018).  We need to start creating a framework to facilitate more inclusive training sets to enable developers to design ethically, instead of looking for blind spots and vulnerabilities of people’s perception and allowing companies to influence what people do and the decisions that they make without them realizing the implications.






References

Buell, S. (2018).  MIT Researcher:  Intelligence has a race problem and we need to fix it.   
     intelligence-race-dark-skin-bias/

Broussard, M. (2018).  Artificial unintelligence: how computers misunderstand the
      world.  Cambridge, MA: MIT Press.

Jackson, J. (2018). Algorithmic Bias. Journal of Leadership, Accountability and Ethics,
      15(4), 55-65.

Ocaña-Fernández, Y., Valenzuela-Fernández, L. A., & Garro-Aburto, L. L. (2019).
     Artificial Intelligence and its Implications in Higher Education. Propósitos y
     Representaciones, 7(2), 536–568. https://doi.org/10.20511/pyr2019.v7n2.274

Reynolds, M. (2017). Bias test to keep algorithms ethical. New Scientist, 234(3119), 10.

Wilson, T. (2018). Artificial unintelligence. How computers misunderstand the world.
     Information Research, 23(2), Information Research, Vol.23(2).

Yanisky-Ravid, S., & Hallisey, S. K. (2019). “Equality and Privacy by Design”: A New
     Model of Artificial Intelligence Data Transparency via Auditing, Certification, and
     Safe Harbor Regimes. Fordham Urban Law Journal, 46(2), 428–486. Retrieved from
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