For WizardKim-DPR1 T1
Week 2 Discussion Post Topic 1 Response :
Instructions:
Respond to the post below with any inputs or suggestions.
· All posts (both initial and responses) must be substantial (several paragraphs each) and each of your initial posts must be supported by 3 peer reviewed or authoritative sources, not including the textbook, cited properly in APA format.
Responses
should have proper support with at least 1 different source as applicable.
Currently, many companies from different sectors are looking for ways to exploit their data in order to improve their operations and consequently obtain greater benefits from them. Over the years, the data processed by organizations and companies have experienced a substantial increase in volume, speed, and variety, so selecting the appropriate technology to process it and take advantage of the knowledge generated by it is crucial in the future of these entities.
Artificial Intelligence (AI) has great potential to solve a wide spectrum of real-world business problems, but the lack of trust from the perspective of potential users, investors, and other stakeholders towards AI is preventing them from adoption. To build and strengthen trust in AI, technology creators should ensure that the data which is acquired, processed, and being fed into the algorithm is accurate, reliable, consistent, relevant, bias-free, and complete. Similarly, the algorithm that is selected, trained, and tested should be explainable, interpretable, transparent, bias-free, reliable, and useful. Most importantly, the algorithm and its outcomes should be auditable and properly governed. (Srinivasan, et al., 2020)
Companies need to understand their desired outcomes to select the right data. Then they need to know exactly where the data comes from, how it was collected and what it represents. That information is key to understanding the context of such data and building trust in the quality of the data being employed in strategy and planning. (Rizzo, 2019)
The payoff to an effective data management strategy is the value of the trusted information. This information can provide advantageous competitive insight, enable sophisticated business performance management, increase employee productivity and satisfaction, and deliver a superior customer experience. The ultimate benefit will be visible in revenue growth, improved operational efficiency, increased customer and employee retention, and increased profitability. (Sidi, et al., 2013)
We need to demonstrate integrity, honesty, and transparency as to what happens to data and what level of control people can, or cannot, expect. We must embed ethical rigour in all our data-driven processes. We must guarantee that data analysis and storage are not compromised by data breaches that reveal personal information. Sanctions for such breaches must be clear with meaningful effect. Development of solutions whereby security is achieved in concurrent layers is required: reducing data travel, separating personal identifiable data from payload data, using effective anonymization and encryption methods. (Lawler, et al., 2018)
The organizations should take a systematic approach to trust that spans the lifecycle of analytics and is founded on four key anchors of trust: quality, effectiveness, integrity, and resilience. (KPMG, 2016)
Quality:
The initial trust in the data depends primarily on its quality. In order to drive quality in Data &Analytics, organizations need to ensure that both the inputs and development processes for D&A meet the quality standards that are appropriate for the context in which the analytics will be used. In many organizations, questions are raised about choice of data sources and data ‘lineage’ (i.e., where the data originated and what process it took to arrive as input data to a system or decision engine). (KPMG, 2016)
There are many examples of inadvertent quality issues which have had massive knock-on impacts for individuals, organizations, markets, and whole economies. And as analytics move into critical areas of society, such as decision engines for drug prescribing, machine learning ‘bots’ as personal assistants, and navigation for autonomous vehicles, it seems clear that D&A quality is now a trust anchor for everyone. (KPMG, 2016)
Effectiveness:
It means that the outputs of models work as intended and deliver value to the organization. This is the top concern of those who invest in D&A solutions, both internal and external to the organizations. (KPMG, 2016)
Organizations that are able to assess and validate the effectiveness of their analytics in supporting decision-making can have a huge impact on trust at board level. The corollary of this, of course, is that organizations that invest without understanding the effectiveness of D&A may not move the needle on trust or value at all. (KPMG, 2016)
Integrity:
Data without integrity–that is not complete, accurate, and consistent–is not very useful. Standards and governance rules provide a disciplined approach to managing business processes and source applications, reinforcing the “truth” and enabling the seamless sharing of data. (Sidi, et al., 2013)
If algorithms are well ‘trained’, then race or gender biases, for example, can be removed. It stands to reason, therefore, that an effective combination of human and machine can offer fairer, more trusted decisions. If not well managed throughout the D&A lifecycle, algorithms can also introduce unintentional, hidden biases as a consequence of the data on which they have been trained. Automated decision engines can also make the ethical consequences feel emotionally distant to the humans who are nominally accountable. For example, board members may blame misbehavior on a rogue algorithm or claim they could not possibly understand the detail of complex models, and therefore absolve themselves of responsibility. (KPMG, 2016)
Resilience:
Resilience in this context is about optimization for the long term in the face of challenges and changes. Failure of this trust anchor undermines all the previous three. (KPMG, 2016)
Basic resilience is key to winning customer trust. It only takes one service outage or one data leak for consumers to quickly move to (what they perceive to be) a more secure competitor. It also only takes one big data leak for the regulators to come knocking and for fines to start flying. (KPMG, 2016)
Before ending my post, I would like to present the example of Facebook, whose biggest challenge has been to gain the trust of its users.
From the start, Facebook attempted to win our trust by showing us they took privacy seriously. The fact there was at least an illusion of privacy was enough to get a lot of people on board the social media revolution. By default, anything a user shared was shared only with a trusted group of friends, It also offered switches allowing individual aspects of a person’s data to be made public or private. (Marr, 2016)
Facebook has revolutionized the way we communicate with each other online by allowing us to build our own network and choose who we share information about our lives with.
This data holds tremendous value to advertisers, who can use it to precisely target their products and services at people who are, according to statistics, likely to want or need them. (Marr, 2016)
Gaining the trust of users is essential. Aside from data thefts and such illegal activity, users can become annoyed simply by being subjected to adverts they aren’t interested in, too frequently. So, it’s in Facebook’s interests, as well as the advertisers, to match them up effectively. (Marr, 2016)
References:
KPMG (2016). Building Trust in Analytics. https://assets.kpmg/content/dam/kpmg/xx/pdf/2016/10/building-trust-in-analytics
Lawler, M., Morris, A. D., Sullivan, R., Birney, E., Middleton, A., Makaroff, L., Knoppers, B. M., Horgan, D., Eggermont, A. (2018). A roadmap for restoring trust in Big Data. Lancet Oncology, 19(8), 1014-1015. http://dx.doi.org/10.1016/S1470-2045(18)30425-X
Marr, B. (2016). Big data in practice: How 45 successful companies used big data analytics to deliver extraordinary results. ProQuest Ebook Central
https://ebookcentral.proquest.com
Rizzo, M. (2019, May 30). Implementing A Modern Data-Driven Digital Strategy. Mondaq Business Briefing.
https://link.gale.com/apps/doc/A587117657/GPS?u=lirn99776&sid=GPS&xid=151c5827
Sidi, K. N., Hutchinson, D. A. (2013, September-October). The trusted information payoff: productivity, performance, and profits. Information Management Journal, 47(5), 35+.
https://link.gale.com/apps/doc/A352038803/GPS?u=lirn99776&sid=GPS&xid=2b6d773b
Srinivasan, A. V., de Boer, M. (2020). Improving trust in data and algorithms in the medium of AI. Maandblad voor Accountancy en Bedrijfseconomie [MAB], (3/4), 147+.
https://link.gale.com/apps/doc/A621598415/GPS?u=lirn99776&sid=GPS&xid=2a00bad3