The Ethics of AI: Addressing Bias and Privacy Concerns
~ Aryan Saxena, Atharva Singh, Shruti Prajapati
Introduction
Artificial Intelligence has certainly grown a lot in prominence. Its prevalence has been increasing continuously in healthcare, finance, personal assistance and a lot many other things. Just Imagine a life without AI in today’s world? As there are always two sides to a coin, there are a few significant concerns associated with AI. Biases and privacy concerns in AI have always been on the top of the talk. AI systems learn from large datasets to make predictions or decisions. However, if these datasets contain biased information, the AI algorithms may inadvertently perpetuate or amplify those biases. It is really very important to address biases in AI so that everyone could have unprejudiced and upright outcomes. AI often relies on vast amounts of personal data to make accurate predictions and perform tasks effectively. You might be unaware of the fact that your personal data, browsing history and social media activities are being monitored and used. This definitely raises privacy concerns. If not handled properly, it can lead to unauthorized access, data breaches, or the misuse of sensitive information. Certainly, it is the right of every AI user to know how their data is being used and if their data is in safe hands to mitigate privacy concerns.
How does Bias and Privacy Breach Happen
AI Bias
AI bias occurs when an algorithm produces results that are unfair or discriminatory towards certain groups of people.
There are two common types of AI Bias namely, Data Bias and Algorithmic Bias.
Data bias happens when AI model training data is unrepresentative or biased. The AI model may reinforce biases in judgments and predictions if the training data is distorted or prejudiced.
Algorithmic biases are biases induced during the design and implementation of AI algorithms. This can happen when the algorithm is incorrect or when it is created with specific assumptions or preferences that result in unjust outcomes for some groups of individuals.
Impact of AI bias can be far-reaching and devastating. For eg: a biased algorithm used in hiring could result in qualified candidates being overlooked because of their race, gender, or other factors.
AI bias can also perpetuate existing societal inequalities making it more difficult for marginalized groups to access opportunities and resources. This can lead to a widening of the wealth gap and further entrenchment of systemic discrimination.
AI Privacy Breach
AI Privacy Breach occurs when AI systems improperly store, access and exploit data without the user’s permission. As AI becomes more sophisticated, it is able to collect and analyse vast amounts of data about individuals, including sensitive information such as health records and financial information.
It can happen in scenarios where AI systems are not adequately secured or if there are vulnerabilities in the system, it leaves them susceptible to malware and hacking attacks that could lead to cyber threats and data theft. Moreover, AI Algorithms can sometimes deduce sensitive information about individuals even without direct access to their personal data. By analysing patterns and correlations in large datasets, AI systems can make inferences about personal attributes or behaviours that individuals may not have explicitly disclosed which may lead to crimes such as identity theft, financial fraud, loss of autonomy etc.
Additionally, AI privacy breaches can erode trust in institutions and undermine public confidence in the use of AI. This can have negative consequences for the development and adoption of AI technologies, limiting their potential to benefit society.
Examples
Amazon’s hiring algorithm
In 2018, Amazon developed an artificial intelligence tool to assist with the recruitment process. The system was designed to analyse resumes and rank candidates to help recruiters select the most qualified candidates for the job. However, the Al system exhibited bias against female candidates. The system learned from historical data, which showed that the majority of successful candidates were men. As a result, the system penalized resumes that contained phrases like “women’s,” and “female,” and ranked them lower. Amazon ultimately scrapped the tool because of its bias issues.
IBM’s Watson AI
In 2017, IBM’s Watson AI was used to help predict the risk of recidivism for criminal defendants. However, it was found that the algorithm was biased against black defendants. The algorithm was more likely to predict that black defendants would reoffend, even after controlling for factors such as criminal history and prior convictions.
Facebook-Cambridge Analytica data scandal
In 2018, it was revealed that Cambridge Analytica, a political consulting firm, had harvested the personal data of over 87 million Facebook users without their consent. The data was used to create psychological profiles of voters, which were then used to target them with political ads.
User Data Breach
In 2016, Uber faced a data breach where the personal information of approximately 57 million Uber users and drivers was compromised. The breach, which was not initially disclosed publicly, exposed names, email addresses, phone numbers, and driver’s license numbers.
Case Study
Let us investigate the ethical implications of artificial intelligence (AI). We will be looking at a thought-provoking case study that shines light on the significant ethical challenges regarding bias and privacy in automated hiring systems and untangle the complexities as well as propose potential solutions to these concerns.
Picture this, a sophisticated facial recognition technology that offers enhanced security and convenience. However, when we peel back the layers, we discover a labyrinth of ethical quandaries around bias and privacy. To acquire a better grasp of these problems, consider the real-world example of Clearview AI.
Clearview AI developed waves with their facial recognition software, which was primarily for law enforcement organizations. By scanning social media networks, the software amassed a massive database of billions of photographs taken without explicit permission. However, as the technology grew in popularity, so did concerns about its ethical implications.
How about we go over some of the bias concerns related to this particular AI software?
Racial Bias: Independent investigations revealed that Clearview AI’s face recognition algorithms have significant racial bias, resulting in higher rates of false positives for individuals with darker skin tones. Concerns were raised concerning potential prejudice and its influence on law enforcement tactics as a result of this bias.
Gender Bias: Clearview AI’s facial recognition algorithms displayed gender bias, frequently misidentifying individuals according to their gender presentation, such as women or transgender people. This exacerbated the worries about fair and unbiased identification.
Now, why don’t we observe some of the privacy concerns as well?
Consent and Data Collection: AI’s data gathering techniques sparked questions, since it acquired billions of photographs without the explicit consent. This unauthorized acquisition of personal data gave rise to questions about privacy rights and the ethics of data collection.
Surveillance and Civil Liberties: Clearview AI’s technology has heightened worries about ongoing surveillance because of its large database and widespread deployment. Critics contended that such unbridled use of facial recognition technology jeopardized privacy and encroached on civil liberties, potentially creating a surveillance state.
How to mitigate
Diverse and Inclusive Training Data: It is crucial to guarantee that AI algorithms are trained on varied and representative datasets in order to mitigate bias. This necessitates gathering data from a diverse set of demographics and backgrounds in order for the system to make fair and unbiased during the hiring process.
Algorithmic Auditing and Testing: Regular audits and assessments of the automated hiring system can assist in identifying and rectifying biases. Independent third-party evaluations should be carried out to ensure that the algorithms are not perpetuating discriminatory practices and are in alignment with established diversity and inclusion goals.
Privacy-First Design: Companies should adopt an automated hiring systems with a privacy-centric mindset. This includes putting in place strong data protection safeguards, gaining informed consent from applicants for data collection and usage, and adhering to relevant privacy laws and regulations.
Human Oversight and Intervention: While AI systems can assist with the hiring method, human oversight is still required. To reduce the possibility of biased outcomes, human resources personnel should be involved in the decision-making process, examining and confirming the system’s suggestions.
The automated recruiting system case study shows the crucial ethical challenges regarding bias and privacy in AI-driven hiring procedures. Organizations should try to develop fair and equitable recruiting processes by adopting inclusive training data, performing algorithmic audits, protecting privacy, and retaining human oversight. We can foster diversity, mitigate bias, and uphold privacy rights in the field of automated employment by using AI technology responsibly and ethically.
Work going on
There are some remarkable advancements and initiatives made with regards to AI biases and privacy over the past few years. The introduction of comprehensive privacy regulations, such as the General Data Protection Regulation (GDPR) in the European Union in 2018, has significantly impacted the landscape of privacy protection. These regulations impose strict requirements on organizations handling personal data, including AI systems, and provide individuals with enhanced rights and control over their data. Many companies are going by privacy by design wherein the companies are trying to integrate privacy protections into the design and development of AI systems. Many companies have also been implementing differential privacy techniques where private data points are protected while training the AI model. Companies are giving importance to fairness and transparency in AI algorithms. Many industry leaders and organizations are developing and adopting ethical AI frameworks that encompass considerations for privacy, biases, and other ethical concerns. These frameworks provide guidelines and best practices to ensure responsible development, deployment, and use of AI technologies. Providing users with more rights over their personal data is becoming prominent. Independent third-party assessments are being done in order to reform potential issues related to privacy issues and biases. Industry collaborations, partnerships, and consortia are forming to address privacy and bias concerns collectively. These collaborations facilitate knowledge sharing, research collaboration, and the development of industry-wide standards and best practices.
Future Perspective
We definitely wish to see a very bright future with AI. However, as AI continues to advance, it is essential to critically examine the potential risks and negative implications that could arise. AI systems are only unbiased as the data they are trained on. What if the data is only flawed? Definitely, there are a lot of steps taken towards addressing this issue, but is it enough to solve this issue completely? AI could become as dark as what no one could have imagined if not handled properly. Addressing this issue is a continuous process.
As AI advances, techniques to address bias issues and privacy breaches need to advance parallelly. Moreover, the ethical considerations surrounding AI raise complex questions. For instance, autonomous vehicles face ethical dilemmas when making split-second decisions that may involve sacrificing the lives of passengers or pedestrians. The challenge lies in programming AI systems to make moral judgments in situations that lack clear-cut answers. Companies and AI designers would definitely have numerous methodologies to prevent this, but we can say with almost 90% assurance that they wouldn’t be able to come up with what methodologies can be used to prevent this after around 20 years. Does anyone know what would be the level of complexity of the AI models 20 years from now? That is a big question mark in itself. Then how do we know about the mitigation strategies from 20 years from now. So conclusively we can say that, if AI becomes smart, there should always be smarter techniques to address its issues, no matter what the time frame is. Responsible and thoughtful development and deployment of AI will be key in shaping a future that is beneficial, fair, and sustainable.
