Showing posts with label Data Governance. Show all posts
Showing posts with label Data Governance. Show all posts

Data Governance, Ethics, And Trust.

 



We have a basic understanding of the data we have access to, where we could get it, and what we can do with it once we have it, we need to consider how to prevent it from becoming a liability. 

Data is more and more likely to rank among a company's most valuable assets. 

However, we must never lose sight of the fact that it has two sides to it. 

If you handle it appropriately, it will help you get the necessary insights. 


However, if you don't handle it respectfully, you may easily cut yourself! 

Nearly as many negative effects result from bad data management and planning as there are possible advantages. 

They include things like higher expenses and overhead, a decline in consumer confidence, the development of a negative public image about your firm's ethics or environmental impact, as well as very harsh penalties that might put your company out of business. 

Here we'll examine the critical elements that must be taken into account to make sure your data policy doesn't put you at odds with your clients, the law, or the line between right and wrong. 


We will discuss some of the risks and problems that must be avoided, such as privacy, prejudice, and the effects of AI and technology on the environment. 


  1. Understanding Evolving AI Ethics
  2. The Significance of "Clean" Data And Bias. 
  3. Keeping Data Within The Bounds Of The Law. 
  4. Implementing Effective Data Governance.


Additionally, we'll examine specific circumstances in which failing to act might result in a violation of our moral and ethical duties to society and our clients. 


~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram



References And Further Reading


  1. Roff, HM and Moyes, R (2016) Meaningful Human Control, Artificial Intelligence and Autonomous Weapons, Briefing paper prepared for the Informal Meeting of Experts on Lethal Autonomous Weapons Systems, UN Convention on Certain Conventional Weapons, April, article36.org/wp-content/uploads/2016/04/MHC-AI-and-AWS-FINAL.pdf (archived at https://perma.cc/LE7C-TCDV)
  2. Wakefield, J (2018) The man who was fired by a machine, BBC, 21 June, www.bbc.co.uk/news/technology-44561838 (archived at https://perma.cc/KWD2-XPGR)
  3. Kande, M and Sönmez, M (2020) Don’t fear AI. It will lead to long-term job growth, WEF, 26 October, www.weforum.org/agenda/2020/10/dont-fear-ai-it-will-lead-to-long-term-job-growth/ (archived at https://perma.cc/LY4N-NCKM)
  4. The Royal Society (2019) Explainable AI: the basics, November, royalsociety.org/-/media/policy/projects/explainable-ai/AI-and-interpretability-policy-briefing.pdf (archived at https://perma.cc/XXZ9-M27U)
  5. Hao, K (2019) Training a single AI model can emit as much carbon as five cars in their lifetimes, MIT Technology Review, 6 June, www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/ (archived at https://perma.cc/AYN9-C8X9)
  6. Najibi, A (2020) Racial discrimination in face recognition technology, SITN Harvard University, 24 October, sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/ (archived at https://perma.cc/F8TC-RPHW)
  7. McDonald, H (2019) AI expert calls for end to UK use of ‘racially biased’ algorithms, Guardian, 12 December, www.theguardian.com/technology/2019/dec/12/ai-end-uk-use-racially-biased-algorithms-noel-sharkey (archived at https://perma.cc/WX8L-YEK8)
  8. Dastin, J (2018) Amazon scraps secret AI recruiting tool that showed bias against women, Reuters, 11 October, www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G (archived at https://perma.cc/WYS6-R7CC)
  9. Johnson, J (2021) Cyber crime: number of breaches and records exposed 2005–2020, Statista, 3 March, www.statista.com/statistics/273550/data-breaches-recorded-in-the-united-states-by-number-of-breaches-and-records-exposed/ (archived at https://perma.cc/BQ95-2YW2)
  10. Palmer, D (2021) These new vulnerabilities put millions of IoT devices at risk, so patch now, ZDNet, 13 April, www.zdnet.com/article/these-new-vulnerabilities-millions-of-iot-devives-at-risk-so-patch-now/ (archived at https://perma.cc/RM6B-TSL3)


Implementing Effective Data Governance.



As you've probably inferred from reading the material we've discussed so far, the most important lesson to learn from this is the need of a complete and comprehensive data governance policy. 

We've addressed all the important topics that must be taken into consideration while developing your plan, including data bias and quality, legal and regulatory issues, data security, and, of course, the ethical issues you must address. 

Your strategy will help you arrive at a set of rules that you can use to manage and maintain your data and analytics technology infrastructure overall by taking all of these factors into consideration. 

Data governance refers to accepting responsibility for the moral and legal obligations you have as an individual engaged in the process of releasing the immense power buried inside information. 



It's important to ensure that you are following all applicable laws, have the appropriate permissions and security in place, and have a clear knowledge of who is in charge of maintaining the security, reliability, and correctness of your data. 

Much of this depends on how well you implement a data culture inside your business; given how crucial excellent data governance is to preserving consumer trust, it should be taught in everyone engaged as a fundamental value. 

It should be clear to everyone how important it is to the business and how carefully it should be handled. 



Naturally, your data governance plan should include the steps you will take to make sure you are in compliance with all applicable laws, including frequent audits of these processes that are owned by a designated individual. 



It also explains how permission to use personal data is requested, where these records are kept, and how they can be kept up to date in the event that permission is revoked or new requirements for data use arise that fall outside the scope of previously granted permissions. 

  • If you deploy CCTV cameras, there should be signs in place informing people that recordings are being produced and explaining why. 
  • If you use Bluetooth or RFID to collect information from consumers' mobile devices while they are on or near your property, you must ensure that contracts are in place that clearly state how the information will be used. 
  • Because you are now accountable for it, if you purchase data from third-party vendors, you must ensure that your uses are consistent with the rights granted when the source gathered the data. 

Data governance is really about managing data as the important company asset that it is. 

The same should be true for your data, just as you have procedures and systems in place to make managing your personnel easier. 

A successful and secure usage of data may be achieved by putting a solid data governance structure in place as part of a larger overall data strategy.


~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram


References And Further Reading


  1. Roff, HM and Moyes, R (2016) Meaningful Human Control, Artificial Intelligence and Autonomous Weapons, Briefing paper prepared for the Informal Meeting of Experts on Lethal Autonomous Weapons Systems, UN Convention on Certain Conventional Weapons, April, article36.org/wp-content/uploads/2016/04/MHC-AI-and-AWS-FINAL.pdf (archived at https://perma.cc/LE7C-TCDV)
  2. Wakefield, J (2018) The man who was fired by a machine, BBC, 21 June, www.bbc.co.uk/news/technology-44561838 (archived at https://perma.cc/KWD2-XPGR)
  3. Kande, M and Sönmez, M (2020) Don’t fear AI. It will lead to long-term job growth, WEF, 26 October, www.weforum.org/agenda/2020/10/dont-fear-ai-it-will-lead-to-long-term-job-growth/ (archived at https://perma.cc/LY4N-NCKM)
  4. The Royal Society (2019) Explainable AI: the basics, November, royalsociety.org/-/media/policy/projects/explainable-ai/AI-and-interpretability-policy-briefing.pdf (archived at https://perma.cc/XXZ9-M27U)
  5. Hao, K (2019) Training a single AI model can emit as much carbon as five cars in their lifetimes, MIT Technology Review, 6 June, www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/ (archived at https://perma.cc/AYN9-C8X9)
  6. Najibi, A (2020) Racial discrimination in face recognition technology, SITN Harvard University, 24 October, sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/ (archived at https://perma.cc/F8TC-RPHW)
  7. McDonald, H (2019) AI expert calls for end to UK use of ‘racially biased’ algorithms, Guardian, 12 December, www.theguardian.com/technology/2019/dec/12/ai-end-uk-use-racially-biased-algorithms-noel-sharkey (archived at https://perma.cc/WX8L-YEK8)
  8. Dastin, J (2018) Amazon scraps secret AI recruiting tool that showed bias against women, Reuters, 11 October, www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G (archived at https://perma.cc/WYS6-R7CC)
  9. Johnson, J (2021) Cyber crime: number of breaches and records exposed 2005–2020, Statista, 3 March, www.statista.com/statistics/273550/data-breaches-recorded-in-the-united-states-by-number-of-breaches-and-records-exposed/ (archived at https://perma.cc/BQ95-2YW2)
  10. Palmer, D (2021) These new vulnerabilities put millions of IoT devices at risk, so patch now, ZDNet, 13 April, www.zdnet.com/article/these-new-vulnerabilities-millions-of-iot-devives-at-risk-so-patch-now/ (archived at https://perma.cc/RM6B-TSL3)