Responsible AI Deployment: The Essential Role of Data Management

As artificial intelligence capabilities rapidly advance, their disruptive potential to transform organisations and societies is staggering. AI technologies are being infused across products, services, and operations – enhancing automation, predictive capabilities, and intelligent decisioning in ways that boost efficiency and unleash new opportunities.

However, this AI proliferation comes with significant risks if the technology is not used responsibly. Unintended biases, lack of transparency, vague accountability lines, sensitivity violations, and other ethical pitfalls could undermine trust and produce adverse consequences. That’s why responsible AI – grounded in core principles around accountability, fairness, inclusivity, reliability, privacy, and transparency – is critical for long-term success.

The Core Principles of Responsible AI

While there is still a lack of global consensus around the responsible use of AI tools, Microsoft has emerged as a leader in defining and promoting the ethical use of AI technologies through its Responsible AI Standard. The latest version 2 outlines key principles that organisations should embrace:

Accountability

There must be human accountability for AI generated outcomes across the entire lifecycle – from design through to deployment and ongoing monitoring. Processes should enable auditability, interpretability of how decisions are made, and with adequate human oversight. For example, if a person was to prepare a budget in Excel and one of their formulas was incorrect resulting in inaccurate figures then the responsibility for the mistake lies with them. Similarly, if a person were to use incorrect information provided by AI, then the responsibility to check and correct it also lies with them.

Fairness

Ensuring reliability and avoiding unfair bias are critically important. AI should be designed and used to equitably distribute opportunities, resources and information in ways that prevent unfair bias, discrimination or penalisation of individuals or groups. Proactive steps like representative data collection and applied fairness techniques should be taken.

Inclusivity

AI should be designed and used to benefit and empower everyone equitably, incorporating accessibility from the ground up. It also needs to agnostically accommodate diverse geographic/cultural circumstances.

Reliability and Safety

AI must be rigorously and functionally tested across diverse situations. Investigation and mitigation of potential risks to individuals, businesses and society is paramount, before it can be used as general practice.

Privacy and Security

Privacy must be prioritised by incorporating practices like data minimisation, sensitivity classification, purpose limitation, and security in data collection/usage. Strong data governance frameworks and privacy protection measures are required, particularly when dealing with sensitive personal data used by AI.

Transparency

There must be traceability on how AI generated outputs are used. Model documentation should be created that clearly outlines AI strengths, limitations, intended use case, and training data allowances. Communications around appropriate trust and clarity on when human involvement is required are essential. 

Embedding Responsible AI Through Intelligent Data Management

Adhering to these principles of responsible AI mandates implementing robust data management strategies and capabilities that extend across the entire information lifecycle – from sourcing high-quality training data to monitoring model performance as well as ongoing improvement validation and regression.

By using advanced AI technologies like machine learning algorithms, small / large language models, cognitive services (such as natural language processing), and more, intelligent information management platforms can streamline and optimise key data preparation processes by leveraging responsible AI frameworks, including:

• Automatically finding, classifying and organising data repositories to ensure trusted data provenance
• Identifying, anonymising, and protecting sensitive personal and confidential data to safeguard privacy
• Applying data quality practices and cleansing processes to create reliable  and relevant training datasets
• Incorporating data traceability, versioning, and auditability of data flows by design into AI pipelines
• Continuously monitoring and maintaining data quality to sustain accurate AI generated outputs over time

The EncompaaaS Difference

At EncompaaS, we believe intelligent information management is the essential catalyst to fulfill responsible AI principles and fuel trusted AI innovation.

Having embraced the Microsoft Responsible AI Standard v2 in our information management architecture, and incorporating it into our platform, EncompaaS force exhibits this for our customers by finding, enriching, organising and de-risking structured, unstructured and semi-structured content anywhere in the enterprise – thus generating a trusted data quality foundation to responsibly deploy AI initiatives – safer, faster and smarter.

EncompaaS is uniquely innovative in the way it delivers this, without the need for complex technical understanding. We allow customers to interact with their entire data corpus by asking simple questions, which EncompaaS in turn selects the right tool to provide the best answer, complete with full auditability, confidence levels, and reasoning. This includes the ability to configure prompts for AI models (such as GPT4) making decisions such as risk, ownership, sensitivity, over-retention, and more much quicker and easier to answer.

Once indications and suggestions are provided by the various services, EncompaaS facilitates a responsible framework for information managers to review, agree, or correct suggestions, providing an evolutionary quality assurance framework aligned with our customers’ risk tolerance and trust, delivering clarity and accountability at scale.

To ensure transparency and to support human oversight of AI generated suggestions, EncompaaS presents details of the confidence levels for each suggestion along with the reasoning behind it.

This level of transparency is further enhanced by presenting potential alternative suggestions above a certain (and fully configurable) confidence threshold, highlighting the confidence level separation between suggestions, which allows users to identify potential performance issues, “false positives”, as well as safety and privacy concerns, ultimately reducing unintended outcomes.

Build Trust and Resilience Through Responsible AI

As AI’s influence continues to reshape our world, public trust in the technology will hinge on organisations proactively prioritising responsible, ethical, and human-centric approaches that incorporate transparency and accountability.

Establishing robust information management practices that uphold responsible principles will be imperative for building trusted and resilient AI capabilities that drive sustainable innovation into the future.

To learn more about how EncompaaS can help your organisation achieve data readiness in preparation for the responsible deployment of AI, contact us or book a demo.