AI is reshaping the information management landscape, introducing highly impactful new ways to manage workflows, improve data accuracy, enhance decision-making, and improve overall efficiency. With tools like document capture, workflow automation, and predictive analysis benefitting from the AI boom, organizations orchestrate their information flow faster and more effectively than ever before.
These advancements, however, are not without ethical challenges. In this post, I will outline the ways these ethical challenges can crop up in the intelligent information management (IIM) space and suggest a path forward for the industry that will minimize these challenges.
The Business Case for Ethical AI
Ethical AI use isn’t just about morality; it’s crucial for future-proofing your business amidst the adoption of a brand-new innovation with rapidly changing regulations and public opinions.
Protecting Your Reputation
Consumers and partners value transparency, and as these groups continue to learn about and adopt this technology, demonstrating solid ethical practices can build trust and credibility.
Avoiding Regulatory Penalties
With increasing scrutiny of AI from public officials, compliance with current laws and potential future interpretations and regulations protects your organization from liability while keeping you ahead of the curve as the legal landscape shifts.
Driving Long-Term Success
Ethical AI use is not just about risk mitigation. It can be used as a competitive advantage. Responsible AI use can earn business from skeptical and privacy-conscious buyers, allowing you to tap into additional markets with much more stringent standards.
Effective AI Use Cases in Information Management
Before diving into the ethical challenges of AI in IIM, it’s essential to understand the common use cases AI serves within these platforms.
1. Intelligent Document Capture
AI enables document capture platforms to extract relevant data with unprecedented accuracy and without using templates. It does so using large language models developed around specific document types.
2. Workflow Automation
Implementing AI in automated workflows goes beyond simple task routing by dynamically adjusting workflows based on context or historical data. For example, these systems can predict the approval route an invoice should take and automatically send it with no user input or detect workflow bottlenecks, automatically escalating time-sensitive tasks or reassigning workloads to avoid delays.
3. Predictive Analytics
AI’s unrivaled ability to analyze trends and recognize patterns offers the potential for impactful predictive analysis in IIM systems. This can look like anticipating seasonal spikes in document processing volumes, allocating resources accordingly, highlighting workflow inefficiencies, and helping system admins preemptively optimize processes.
Understanding Ethical Challenges with AI
AI-Related Bias
AI-based decision-making can be prone to bias depending on the quality of the model. This bias can stem from several sources. Training data can significantly affect the quality of an AI model’s decisions if it has not drawn from a diverse enough sample size. Algorithms used in the model may amplify existing biases or create new ones as the model works with additional data in a specific setting. Even development teams unaware of specific circumstances may inadvertently create blind spots.
Potential Examples of Bias in IIM
Examples of bias in AI tools include misclassifying documents or prioritizing certain information during searches. AI analysis may prioritize high-value vendors for payment over smaller ones or flag contracts from non-standard industries as high risk unnecessarily.
Mitigating AI Bias
Despite this challenge, there are effective mitigation strategies to ensure that AI models’ decision-making considers all factors.
- Diverse Training Data
Using diverse data sets gathered from different samples of individuals, organizations, and collectives, AI models will be trained to handle a broader range of situations fairly and effectively.
- Algorithm Audits
Regularly reviewing the algorithms used in AI models will shine a light on any potential for bias and ensure it is learning as intended from each new interaction.
- Explainable AI
Making AI decisions transparent and understandable to stakeholders allows them to understand the logic that AI models use so that additional input can be gathered.
Privacy Challenges in AI
AI systems require large volumes of data to function effectively, often including sensitive or personal information. This data dependency, when mishandled, can create risks. In a rush to attain the needed data, it may be gathered without consent. What’s more, large pools of data increase a server’s value as a target in cyber attacks.
Implementing Privacy Protection Practices
- Data Encryption and Anonymization
Protecting sensitive information from unauthorized access using standard encryption methods ensures that it will not be usable if intercepted by unauthorized sources. In addition, separating any personally identifiable information (PII) from other data points and other PII ensures this information remains confidential.
- Zero-Trust Security Models
Assuming no user or system is inherently trustworthy, organizations will have the scrutiny necessary to protect AI-related data from phishing attacks and other hacking methods reliant on human error.
- Regulatory Compliance
Staying aligned with data protection laws and adhering to standards and regulations like SOC, HIPAA, and FERPA protect user data with industry best practices.
Building Ethical AI Governance
Implementing ethical AI use on an organizational level requires overarching systems and strategies. But by standardizing AI ethics on an organizational level, you are demonstrating a future-minded approach that builds trust and credibility.
● Ethics Committees
Establishing internal oversight to review AI implementations creates checks and balances around ethics and best practices.
● Third-Party Audits
Partnering with external experts for unbiased evaluations adds additional, unbiased input to your AI operations.
● Ethics Training
Providing ongoing education around ethical AI use within your organization ensures all teams are aware and mindful of these practices.
A Call to Action
Now is the time to prioritize ethics in AI. By addressing these challenges head-on, businesses can harness the tremendous benefits of this technology while preparing for the future of this new frontier.