Technology is evolving quickly – and we’re in the midst of a trust crisis as a result. Thus, Gartner advises that the fifth Top 10 Strategic Technology Trend for 2020 is transparency and traceability.
The more knowledge consumers gain regarding how their data is being used, the higher the liability is for organizations when it comes to gathering and storing it. According to Gartner, “transparency and traceability refer to a range of attitudes, actions and supporting technologies and practices designed to address regulatory requirements, preserve an ethical approach to use of artificial intelligence (AI) and other advanced technologies, and repair the growing lack of trust in companies.”
With an emphasis on trust, there are six elements to note: ethics, integrity, openness, accountability, competence and consistency.
1. Ethics — This is an important element to consider when handling personal data and algorithms.
2. Integrity — Organizations should think of ways to reduce or eliminate bias and the mishandling of personal data when creating systems. This requires integrity.
3. Openness — Ethical principles and privacy commitments must be concise and accessible. Furthermore, if policy undergoes change, “the appropriate constituencies” should be included in the decision-making process.
4. Accountability — Gartner wants you to ask yourself if there are mechanisms for testing, assurance and auditability that allow privacy or ethical concerns to be addressed, whether it’s adherence to regulatory requirements, or concerns that stem from new technologies.
5. Competence — Gartner also wants you to consider whether the enterprise has “implemented design principles, processes, testing and training.” This is necessary in order for constituencies to regain confidence that the company will deliver successfully.
6. Consistency — Consistency is key when it comes to policies and processes.
Algorithmic decisions are paramount to an organization’s and consumer’s every move. Whether it’s recruiting or buying products and services, the wrong algorithm can lead to serious legal issues, including “gender and racial bias and other forms of discriminatory activities.”
Taking the necessary steps to eliminate bias in the AI community is just as crucial. Explainable AI “is the set of capabilities that describes a model, highlights its strengths and weaknesses, predicts its likely behavior, and identifies any potential biases.”
In addition, explainable AI can easily ensure accuracy, fairness, accountability, stability and transparency in algorithmic decision making by fluently communicating the decisions of descriptive, predictive or prescriptive models.
It also supports AI governance, a process that involves “assigning and assuring organizational accountability, decision rights, risks, policies and investment decisions for applying AI, predictive models and algorithms.”
When talking about transparency and data privacy, it’s important to mention the General Data Protection Regulation (GDPR), which deals with the privacy and protection of personal data of European Union (EU) citizens. Although GDPR appears to be a law applied only in Europe, data protection laws are expanding in several U.S. states.
According to Investopedia, here are some of the GDPR requirements:
· Visitors should be notified of any data the site gathers from them, and give their consent.
· Visitors must be notified promptly if the personal data that the site collected from them has been breached.
· An assessment of the site's data security is necessary.
· Visitors should easily be able to access information on how to reach important contacts, such as the data-protection officer (DPO), in order to exercise their EU data rights.
· Personally identifiable information (PII) collected by sites should be anonymized or pseudonymized.
While the introduction of GDPR has had a considerable impact on how businesses store and use the data that belongs to their customers, it also has a positive effect on customer experience. GDPR has given people power over the ownership of their data, requiring companies to show customers what they will do with it. Instead of being a hindrance to businesses, the introduction of GDPR has provided an opportunity to develop more meaningful relationships with the public. Businesses must now offer added value in return for their customers’ data, and in many cases, this value takes the form of an improved, more personalized customer experience.
You should reevaluate your business processes to ensure they’re completely transparent and traceable. You’ll build customer trust and set yourself apart from the competition as a result. Consider learning the differences between data integrity, data quality, and data trust in order to address concerns quickly and prevent problems before they even occur.
1. Data Integrity – Refers to the overall completeness, accuracy, and consistency of data. Is the data correct? Can it be replicated and provide the same results each time? These are all questions to be addressed to ensure the integrity of your data.
2. Data Quality – Refers to a clear understanding of the meaning, context, and intent of the data. Characteristics of data quality can consist of the following: accessibility, timeliness, frequency, granularity, definition, relevancy, and comprehensiveness.
3. Data Trust – Refers to the ability of the recipient to accept the data from the person or group delivering it. The person who delivers the data should be a subject matter expert with the expertise to provide answers and provide confidence in the data.
To further explore data integrity, check out our article “Data Integrity: Methods to Prevent Data Concerns” or contact Stefanini now to learn more.
With over 30 years of expertise in the technology market, Stefanini drives business in companies of all sizes.
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