HI5031 Professional Issues in IS Ethics and Practice Case Study Sample
Assignment Details
Individual Case study submission structure is as follows:
You need to submit the final version of your assignment in week 9.
Case Study Question: Ethical Dilemmas in Artificial Intelligence Development
You are a senior software engineer at a tech company tasked with developing an AI- powered recommendation system for an e-commerce platform. During the development process, you encounter ethical dilemmas related to algorithmic bias, user privacy, and transparency. Discuss the ethical considerations and decision-making process you would undertake to address these issues while ensuring the ethical and responsible deployment of AI technology in a commercial setting. Provide specific examples and recommendations based on ethical frameworks and best practices in the field of information systems ethics.
Here's a typical structure for this case study:
1. Introduction
2. Identification of Ethical Issues
3. Ethical Frameworks
4. Decision-Making Process
5. Recommendations
6. Ethical Considerations in Implementation
7. Conclusion
8. References
The word Count for this scenario is 2500 words excluding references. Your report should be a single MS Word or Open Office document. Please do not use PDF as a submission format. The report will require to have at least 10 references. All citations should be provided using adapted Harvard referencing.
Solution
1. Introduction
Artificial intelligence (AI), has emerged as a unique technology for many reasons (Brendel et al, 2021, p1(1)). The key functions performed by AI are learning, perceiving, interacting, reasoning with the environment, problem-solving, decision-making, and even demonstrating creativity. The immense potential of AI to automate tasks and regulate faster advancements has enhanced its acceptability in organisational management which is why it has ushered as one of the biggest investments in business. As per statistical estimates, the global market value of AI has reached USD 150.2 Billion in 2023 due to the mounting investments into AI-based technologies (MarketsandMarkets, 2023). On the other hand, the exponential usage of AI-based technologies and services has increased the organisational responsibilities to assume ethical considerations to avoid potential consequences of AI. Ethical considerations of AI encompass the embodiment of management decision-making to state the transparency and traceability of business. Additionally, The importance of ethical considerations in AI mainly emphasises on strategic implementations to surpass ethical dilemmas in AI.
The complex chain of AI applications raises questions on ethical concerns which substantially influences the overall organisational procedures and reveals the vulnerabilities. This report aims to deliver a comprehensive explanation of these Ethical dilemmas severely impacting the operational efficiency of a Tech company trying to develop an AI-powered recommendation system for an e-commerce platform. In order to state the complete matter of concern, a structured way will be followed which shall include the Potential ethical issues which can be resolved using ethical frameworks and holistic decision-making processes. Thereafter, the recommendations will also be provided along with the ethical considerations in order to overcome such dilemmas again in future.
2. Identification of Ethical Issues
The dynamic and ever-evolving competition in the e-commerce industry has boomed exponentially due to the advent of contemporary digital technologies like AI. Additionally, the sustenance in this enormous competition has pushed the e-commerce sectors towards the implementation of leveraging technologies such as AI-generated recommendation systems to exceed operational efficiency and customer satisfaction. AI-powered recommendation systems are extraordinary digitised tools meant to generate customised support to consumers by evaluating their previous buying behaviours. Moreover, these recommendation systems are beneficial for predicting the prospective current preferences for specific goods. AI-enabled recommendation systems are integrated with Algorithms, Machine learning and Computational Intelligence to improve predictive analysis, cold start issues and resolve data sparsity (Silcox et al, 2024, p1(3)). Recommender systems are mainly devised to aid individuals who have less shopping experience or knowledge to deal with the huge array of choices they are presented with. Moreover, this AI-generated tool has become an indispensable supportive system to accelerate e-commerce services by reducing the information load. However, during the process of developing this tool for a renowned e-commerce organisation, the software engineer of the tech company encountered several ethical dilemmas depicted in the following discussions for university assignment help.
Algorithm Bias: Algorithms have emerged as a critical factor of the digital economy which in response underpinned the data-driven innovation to transform human lives. However, Algorithm based tools come with different ethical risks such as fairness, transparency and accountability (Gustilo et al, 2024, p1(1)). Algorithm bias is one such critical ethical challenge that is predominantly deep-rooted in representative databases. Deep learning and Machine learning Algorithms are two of the critical organs of AI which have gained rapid prominence because of their capability to process high volumes of data. However, one of the potent weaknesses of algorithms is their inability to scrutinise casualty. Instead of this, Algorithms identify correlations on the basis of potential terms which results in analysing meaningless interrelations resulting in inconclusive evidence. Moreover, Algorithm oriented bias can be estimated as a problematic thing of algorithmic findings that might cause an adversarial effect on unprotected or protected groups because of inappropriate models that miss associations between input features and output variables. The algorithm based biassed outputs results from malfunctioned inputs or framework characteristics, if fed back into the algorithm for tuning purposes, may initiate the existing biases in the system (Tian et al, 2024 p1(5)). Therefore, it can be assessed that Algorithm bias can result in incidents like associating first names of a customer to different account holders which in response can deteriorate the brand image of the e-commerce platforms.
Privacy concerns for users: Maintenance of users’ privacy concerns has enhanced as one of the most challenging threats uncovered during the formation of AI generated: Recommendation system. Users’ privacy is an inevitable ethical consideration that ensures the responsible and proactive attitude of e-commerce to secure the humongous private data of consumers. Most of the commercially successful e-commerce have implemented AI-generated recommendation systems relying upon collaborative or hybrid filtering techniques to produce customised recommendations (Milano et al, 2020, p961(1)). During the application of this process, privacy concerns occur in mainly four stages. The first example of a privacy breach in an AI-powered recommendation system can be the collection or sharing of explicit and private data of the customers without their prior concerns (Bouderhem, 2024, p6(2)). Thereafter, the Cloud-based data storage systems of AI-based recommendation tools are more likely to be attacked by de-anonymization attempts. Both of these scenarios of privacy breaches violate the Individual right against violation and endanger the autonomy of customers to decide to share private information. Additionally, the system inference of AI-powered recommendation systems also raises the risk of privacy breaches. This happens due to the autonomous power of system inferences to draw the personal information of consumers for predictive analysis without obtaining the proper permissions.
Transparency issues: The necessity for transparency in the predictive systems based on ML algorithms is enhanced as a consequence of their ever-increasing proliferation in any industry. The error of transparency in AI-generated decision-making and automated recommendation systems highlights problems between transparency as a normative ideal and its implication to practical application (Felzmann et al, 2020, p3334(2)). The biggest reasons behind the circumvention of the Transparency issue during the development of the AI-generated Recommendation system have been over-explainability in the databases and the swap between the AIk performance and these explanations.
3. Ethical Frameworks
Accountability: Algorithm bias significantly influences the decision-making accuracy of the AI-powered Recommendations systems which in response can lead to a disparate impact on the entire predictive analytics system of the e-commerce. Additionally, flawed algorithms can also produce repetitive errors in data-driven innovations. Thus, to overcome such procedural irregularities, the Incorporation of an Accountability Framework of Ethics can provide effective guidelines. Algorithmic impact assessments (AIAs) can be regarded as an emergent form of accountability for corporate organisations that demonstrate and deploy automatic support in decision-making (Koumpouros, 2024, p735(1)). AIA is a renewed ethical framework for the delineation of Accountability by the rendering of practical solutions to ameliorate the potential harms caused due to biased algorithms.
Monitor and Performance Tracking: Algorithm-based services are consistently featured within an ecosystem of complex and socio-technical challenges, which can obstruct the autonomy and rights to privacy of the users (Tsamados et al, 2021, p222(2)). The pervasive distribution and proactivity of algorithms and their complex mechanism can infringe on users' privacy. Henceforth, to surpass these immoral challenges, Performance tracking, Auditing and Monitoring can work as an essential ethical pillar to upgrade the preventive measures for ensuring users' privacy concerns. These methods involve the explanations of those situations which have led to breach of privacy of the customers. To ensure performance tracking and systematic monitoring, the installation of different kinds of metrics such as AUC, ROC Curve, R2, and MSE can be beneficial.
Interpretability of Algorithms: The ethical framework interpretability denotes the intrinsic attributes of a deep model measuring to which degree the inference outcomes of the deep model are understandable or predictable to human beings (Li et al, 2022, p3197(3)). Thus, it can be evaluated that the interpretability of algorithms is meant to promote trustworthiness which affects the reliability of algorithm-based recommendation systems. Therefore, to disseminate transparency, deep learning-based interpretable tools can be attached to avoid the ethical dilemmas of using AI-based recommendation systems.
4. Decision-Making Process
Regular Audit for Preventing Algorithm Bias: Regular auditing is one of the best practices to be incorporated into the decision-making processes by the software engineer of the tech company to prevent Algorithm biases. Along with that, persistent auditing and monitoring can also check the transparency and traceability of the e-commerce company to mitigate the prospective ethical dilemmas of using AI. Audits are focused primarily on matters of transparency or autonomy (Boag et al, 2024, p20(2)). Thus, when algorithms-based recommendation systems track consumer behaviours digitally or track their previous buying patterns to cumulate personalised ads, the Audit will focus on identifying the potentially biased information. Moreover, the decision-making process must emphasise on using Auditing in three selective ways. The first way should be thorough assessments by the regulatory auditors to evaluate whether the algorithms have followed ethical or legal standards. Secondly, ethical auditing must ensure providing sufficient scope to customers or suppliers and vendors to control reputational or unethical misconduct. Furthermore, the internal stakeholders must be aware of Regular auditing for the ethical assessments of algorithms. This will encourage the incorporation of making well-informed decisions regarding responsible engagements with the customers.
Involvement of Cross-Functional Team of Expertise: The appointment of cross-functional teams in the adhesion of transparency, consumer privacy and mitigation of algorithm bias is the second important attribute to be included by the software engineer while making decisions. Addressing the ethical lapses due to the usage of AI tools is a critical challenge that requires not just expertise but also skills and knowledge of diverse subjects. Cross-functional collaboration is a vital element in developing a more responsible and fair AI system within a corporate organisation (Bao & Zeng, 2024, p712(2)). Henceforth, appointing various experts in AI and Algorithms can help the software engineer in addressing the blind spot of error that might lead to discrepancies in following ethical conduct.
5. Recommendations
? Transparency and Traceability: The world is transforming due to the usage of AI whereas Algorithmic decision-making has emerged from the activities of daily life in an omnipresent way (Bruder & Baar, 2024, p1(2)). However, the black-box tendency of AI systems has increased several ethical issues. In order to mitigate these ethical challenges, Transparency is considered as a core ethical element. In the context of machine learning, both the explainability and transparency are considered as the fundamentals of the AI systems that have ethical background. Henceforth, ensuring transparency will be the more appropriate and accountable measurement in comprehending the algorithmic decisions taken in the AI-generated recommendation system. Moreover, Transparency will ensure more informed and ethical concerns of the consumers.
? Security and Privacy: Information technology is essentially based on gathering relentless information (Jung et al, 2024, p3(7)). However, it comes with events like cyber attacks and data breaches as well. Specifically, AI-generated recommendation systems are known for vulnerabilities towards cyber-attacks. Hence, to protect such misconduct and ensure consumers' privacy, the implementation of AAI-based tools can be beneficial.
? Accountability: Accountability refers to a goal that contributes in developing well-governed information systems and holds demanding values in the contexts of governance as well (Shick et al, 2024, p1(5)). Proper usage of information systems and AI can increase the certainty of algorithmic biases but it can also add value in the decision-making approaches to improve overall transparency and traceability of the AI-powered recommendation system. Moreover, Accountability ensures that the autonomy is acceptable (Bera & Rahut, 2024, p1(3)). In order to enhance Accountability and traceability, the software engineer must appoint expert personnel who appropriately understand the operational and ethical methods of AI including the transparent system of managing documentation to safeguard data breaches.
6. Ethical Considerations in Implementation
Some of the strategies mentioned above can be deployed in an e-commerce platform by keeping in mind all ethical considerations.
? Implementation of Transparency: To maintain transparency in the deployment of AI tools in e-commerce it can be said that there should be data transparency in its entirety (Gilbert et al, 2024, p2(2)). The collection of the data along with the ways of usage of the data should be transparent enough. The users should be informed about how the data is collected and used. There should be transparency in algorithms as well. The algorithms used and how decisions are taken should be explained as well. The consent of the users should be taken before using their data as well as the purpose behind it as well. The testing and as well as validation of the AI mechanisms should be unbiased and it should address all issues related to transparency.
? Implementation of Privacy and Security: Privacy and Security is one of the utmost measures that should be followed before the deployment of Artificial Intelligence in the online shopping industry. The design of the AI systems should be private and there should be protections for it so that it should remain private (Gilbert et al, 2024, p34(3)). This should be done so that the information of the users which is sensitive along with minimisation techniques of data and anonymisation can be kept private (Gilbert et al, 2024, p34(3)). The data security should be quite robust and appropriate so that there is no risk of data breaches after the deployment. Along with that, there should be no discrimination in the outcomes after the implementation of AI. The ethics should be followed by regular monitoring of the system. The AI infrastructure should be secure enough so that there should be no threat of cyber threat. The GDPR of the country should also be followed so that the new AI can be deemed ethical.
? Implementation of Accountability: Accountability of an ethical newly deployed AI is inherent in its governance structure. The structure of governance that will be formed in the monitoring of the newly formed AI mechanism should be clear and have a huge dedication to establishing the code of ethical standards (Naguib et al, 2024, p6(4)). The individuals appointed to the governance board should be dedicated enough to look into the matter with precision. There should be a whole lot of training which will create such an awareness among the individuals. Finally, there should be a risk assessment committee that will help in understanding the risks involved and try to solve them thereby bringing the accountability that is needed.
7. Conclusion
Thus, it can be said that there are a whole lot of ethical biases that are present during the deployment of any new mechanism. Artificial intelligence being a new technology has its advantages and disadvantages as well. Therefore, it is bound to have many ethical dilemmas. This report has put forward multiple ethical dilemmas like algorithm bias, transparency issues and non-maintenance of privacy and security. Various ethical frameworks and decision-making tactics have been discussed in this report to get over these dilemmas. Finally, three recommendations have been provided and the ethical ways of implementing those have been portrayed as well. The recommendations were like maintenance of transparency, security and also accountability.
8. References
Bao, A., & Zeng, Y. 2024. Understanding the dilemma of explainable artificial intelligence: a proposal for a ritual dialog framework. Humanities and Social Sciences Communications, 11(1), 1-9. https://www.proquest.com/scholarly-journals/understanding-dilemma-explainable-artificial/docview/2931865026/se-2
Bera, S., & Rahut, D. 2024. Technology development pathways: enigmas of appropriate technology choice. Discover Sustainability, 5(1), 45. https://www.proquest.com/scholarly-journals/technology-development-pathways-enigmas/docview/3013907115/se-2
Boag, W., Hasan, A., Kim, J. Y., Revoir, M., Nichols, M., Ratliff, W., ... & Sendak, M. 2024. The algorithm journey map: a tangible approach to implementing AI solutions in healthcare. npj Digital Medicine, 7(1), 87. https://www.proquest.com/scholarly-journals/algorithm-journey-map-tangible-approach/docview/3034864588/se-2
Bouderhem, R. 2024. Shaping the future of AI in healthcare through ethics and governance. Humanities and Social Sciences Communications, 11(1), 1-12. https://www.proquest.com/scholarly-journals/shaping-future-ai-healthcare-through-ethics/docview/2957630529/se-2
Brendel, A.B., Mirbabaie, M., Lembcke, T.B. & Hofeditz, L. 2021. Ethical management of artificial intelligence, Sustainability, 13(4), p.1974. https://www.proquest.com/scholarly-journals/ethical-management-artificial-intelligence/docview/2489963530/se-2
Bruder, M. & Baar, T. 2024. Innovation in humanitarian assistance—a systematic literature review. Journal of International Humanitarian Action, 9(1), p.2. https://www.proquest.com/scholarly-journals/innovation-humanitarian-assistance-systematic/docview/2914338318/se-2
Felzmann, H., Fosch-Villaronga, E., Lutz, C. and Tamò-Larrieux, A. 2020. Towards transparency by design for artificial intelligence, Science and engineering ethics, 26(6), pp.3333-3361. https://www.proquest.com/scholarly-journals/towards-transparency-design-artificial/docview/2473395212/se-2
Gilbert, S., Baca-Motes, K., Quer, G., Wiedermann, M. & Brockmann, D. 2024. Citizen data sovereignty is key to wearables and wellness data reuse for the common good. npj Digital Medicine, 7(1), p.27. https://www.proquest.com/scholarly-journals/citizen-data-sovereignty-is-key-wearables/docview/2925319110/se-2
Gilbert, S., Baca-Motes, K., Quer, G., Wiedermann, M., & Brockmann, D. 2024. Citizen data sovereignty is key to wearables and wellness data reuse for the common good. npj Digital Medicine, 7(1), 27. https://www.proquest.com/scholarly-journals/citizen-data-sovereignty-is-key-wearables/docview/2925319110/se-2
Gustilo, L., Ong, E. & Lapinid, M.R. 2024. Algorithmically-driven writing and academic integrity: exploring educators' practices, perceptions, and policies in AI era. International Journal for Educational Integrity, 20(1), p.3. https://www.proquest.com/scholarly-journals/algorithmically-driven-writing-academic-integrity/docview/2968690193/se-2
Jung, H.J., Tian, X. & Oh, K.W. 2024. Sustainable threads: analyzing the impact of corporate social performance, CSR message authenticity and information transparency on purchase intentions. Fashion and Textiles, 11(1), p.12. https://www.proquest.com/scholarly-journals/sustainable-threads-analyzing-impact-corporate/docview/2938145035/se-2
Koumpouros, Y. 2024. Revealing the true potential and prospects of augmented reality in education. Smart Learning Environments, 11(1), 2. https://www.proquest.com/scholarly-journals/revealing-true-potential-prospects-augmented/docview/2911658671/se-2
Li, X., Xiong, H., Li, X., Wu, X., Zhang, X., Liu, J., Bian, J. and Dou, D. 2022. Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond. Knowledge and Information Systems, 64(12), pp.3197-3234. https://www.proquest.com/scholarly-journals/interpretable-deep-learning-interpretation/docview/2727081952/se-2
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Milano, S., Taddeo, M. & Floridi, L. 2020. Recommender systems and their ethical challenges, Ai & Society, 35, pp.957-967. https://www.proquest.com/scholarly-journals/recommender-systems-their-ethical-challenges/docview/2471891104/se-2
Naguib, H.M., Kassem, H.M. & Naem, A.E.H.M.A. 2024. The impact of IT governance and data governance on financial and non-financial performance. Future Business Journal, 10(1), pp.1-22. https://www.proquest.com/scholarly-journals/impact-governance-data-on-financial-non/docview/2917423478/se-2
Shick, A. A., Webber, C. M., Kiarashi, N., Weinberg, J. P., Deoras, A., Petrick, N., . & Diamond, M. C. 2024. Transparency of artificial intelligence/machine learning-enabled medical devices. NPJ Digital Medicine, 7(1), 21. https://www.proquest.com/scholarly-journals/transparency-artificial-intelligence-machine/docview/2918404617/se-2
Silcox, C., Zimlichmann, E., Huber, K., Rowen, N., Saunders, R., McClellan, M., ... & Bates, D. W. 2024. The potential for artificial intelligence to transform healthcare: perspectives from international health leaders. npj Digital Medicine, 7(1), 88. https://www.proquest.com/scholarly-journals/potential-artificial-intelligence-transform/docview/3034864734/se-2
Tian, J., Jin, Q., Wang, Y., Yang, J., Zhang, S. & Sun, D. 2024. Performance analysis of deep learning-based object detection algorithms on COCO benchmark: a comparative study. Journal of Engineering and Applied Science, 71(1), p.76. https://www.proquest.com/scholarly-journals/performance-analysis-deep-learning-based-object/docview/2969629248/se-2
Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M. & Floridi, L. 2021. The ethics of algorithms: key problems and solutions, Ethics, governance, and policies in artificial intelligence, pp.97-123. https://www.proquest.com/docview/2595164566/9F85FDE9954646AFPQ/2?sourcetype=Books
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