Ethical Considerations in Predictive Analytics: Ensuring Fairness and Accountability

Organizations across industries are mining Big Data for information and using it in advanced analytics like predictive modeling. Predictive analytics uses historical data and machine learning algorithms in order to identify patterns and forecast future outcomes - be they events or behaviors. 

Predictive analytics is proving to be immensely beneficial in both the private as well as the public sector. In the private sector, it helps improve inventory management, optimize marketing campaigns and personalize customer experiences. In the public sector, it aids in the management of government finances and services more efficiently, helps monitor health trends and improves transport safety. Fraud detection by law enforcement agencies and predicting patient outcomes better in healthcare are its other benefits, among many others. 

According to Precedence Research, the global predictive analytics market size was valued at USD 10.2 billion in 2022, and it is expected to hit at around USD 67.86 billion by 2032 with a registered compound annual growth rate (CAGR) of 21.4% during the forecast period 2023 to 2032. 

MarketsandMarkets believes that factors such as increasing use of AI and ML, acquisitions, penetration of 5G and product launches are expected to drive the adoption of predictive analytics software and services. 

Ethical Considerations in Predictive Analytics

According to Exploding Topics, around 2.5 quintillion bytes worth of data are generated each day. More importantly, 70% of the world’s data is user-generated! Data comes from and belongs in a social context, hence the ethics of data collection, mining and the use of results thereof have to be seen within a social context. 

Any kind of research design and practice should be governed by ethical considerations - principles which cover aspects like voluntary participation, informed consent, confidentiality, etc. These principles, in predictive analytics, circle around issues like data privacy, bias, transparency and accountability.  

Data Privacy

One of the main ethical concerns with predictive analytics is data privacy. The data that drives predictive analytics is oftentimes collected from people without their knowledge or consent. 

Pertinent questions about access, security and misuse of information are being asked, as predictive analytics gains momentum across industries. 

• What if the detailed insights into human behavior are perceived by people as a breach of their privacy?

• What when personal data is misused and exploited for purposes beyond its original intent, like intrusive adverts or profiling?
• What about breaches and unauthorized data access, leading to financial losses, identity theft and so on?

Organizations are coming up with potential solutions to such concerns, through careful planning and application of privacy principles.

1. Data Governance and Access Management - These organizational structures provide clear directions as to how the data is to be handled and protected by the organization. Data owners and procedures are defined. An access management regime is set up.
2. Compliance Programs - Big Data compliance programs monitor and review compliance with regulatory and contractual commitments.
3. Data Sharing Provisions - These help maintain an organization’s responsibility towards their customers when sharing data with third parties. 

In our view, compliance-based approaches to privacy protection tend to focus on addressing privacy breaches after the fact. Instead, we recommend that organizations build privacy protections into their technology, business strategies and operational processes to prevent breaches before they happen. - Protecting Privacy in the Age of Big Data and Analytics, Deloitte

AI Bias

Through the many stages of development, bias can accidentally enter predictive models. Historical information carries old prejudices and predictive analytics algorithms are thus only as good as the data they are trained on. If the data itself is biased, the algorithm will be biased as well, and predictive analytics models will end up perpetuating the biases. 

The consequences may be unintended but impeding truth can affect people’s opportunities and lives, through the reinforcement of:

• Socioeconomic inequalities
• Racial, gender or ethnic prejudice
• Discrimination

“If you make a technology that can classify people by an ethnicity, someone will use it to repress that ethnicity.” Clare Garvie, senior associate at the Center on Privacy and Technology at Georgetown Law.

Some of the ways to create inclusive predictive analytics systems are:

1. Comprehending the prevalence and influence of bias.
2. Knowing the sources of bias.
3. Ensuring ‘algorithmic fairness’, that is creating models without prejudice to lessen differential effects. 
4. Inclusive data collection, which keeps diversity in mind and predictions more accurate and balanced. 
5. Transparent models which enable stakeholders to study predictive conclusions and correct biases.
6. Model evaluation by social groups.

Transparency

Transparency is an important ethical consideration in predictive analytics and carries within its folds the ideas of informed consent and privacy. Data transparency essentially means honest disclosure of information, consistent presentation of facts and figures, and making data easily accessible to everyone.

Ideally, informed consent should be a prerequisite when collecting data. Participants should know the purpose of data collection, how the findings will be used, and who will have access to the findings. That is what defines ‘informed’ in informed consent - whether they want to participate in the study at all. Whether or not they want to allow the ‘discovery of volunteered truths’ about themselves. 

Transparency in data collection is also important to build and maintain trust between consumers and businesses. Consider the collection of health-related data of patients in a hospital. Patients have a right to know, even if they do not fully comprehend, how their data is used and their health predictions made. If opacity exists, a lack of trust in healthcare providers as well as the system ensues. 

Accountability

Think about this:  

• If a predictive model makes an incorrect prediction that leads to a negative health outcome, who is responsible? Is it the healthcare provider who used the prediction? The data scientist who developed it? Or even the company which provided the predictive analytics software? 
• Or, if an organization is found to be using biased algorithms, should they be held accountable for their actions and required to take steps to address the issue.

Complex questions like these with no easy answers abound. 

Predictive models become very powerful when married to machine learning, as ML “learns” from the data that is fed. Where there is so much power, can accountability be ignored? Both, the developers of the algorithms as well as the organizations which are using them are accountable. Meanwhile, we need to have frameworks, legislation, and systems in place to mitigate some of the negative impacts that might arise from the use of predictive models.

Ethical Concerns v/s Analytical Objectives

‘We know there’s a cost when models predict incorrectly, but is there also a cost when they predict correctly? It’s a real challenge to draw the line as to which predictive objectives pursued with machine learning are unethical, let alone which should be legislated against, if any. But, at the very least, it’s important to stay vigilant for when machine learning serves to empower a preexisting unethical practice, and also for when it generates data that must be handled with care.’ 

 Eric Siegel in Harvard Business Review

The era of Big Data and AI is here to stay. In order to ensure responsible use of predictive analytics, organizations like Evon Technologies Pvt Ltd, a software development company in India, are arriving at the right balance between ethical concerns and analytical objectives. Clear governance policies, data ethics training and regular audit and monitoring practices help. To know more, contact us today or drop an email at This email address is being protected from spambots. You need JavaScript enabled to view it..