Policy Needs for AI: Mapping Out Priorities & Goals

The rapid pace of artificial intelligence (AI) advancement presents boundless opportunities for businesses to streamline operations, unlock insights, and better serve customers.

However, without thoughtful governance, AI also poses risks around data privacy, algorithmic bias, and more. Forward-thinking companies must recognize the need to proactively assess policy requirements for responsible AI development and adoption tailored to their use cases.

Crafting a comprehensive corporate AI policy aligns AI initiatives with ethics and core values. It also meets legal compliance mandates while building critical public trust.

This article explains best practices that businesses should follow to map out an AI policy roadmap suited to their priorities and goals. We also highlight how partnering with AI-specialized Google consultancies like Promevo accelerates realizing the benefits of AI safely.

What Policy Needs Should Be Assessed for AI?

Any organization leveraging AI must determine appropriate policies, procedures, and governance models aligned to its objectives. An AI policy assessment examines factors like:

• Legal and regulatory obligations for security, privacy, transparency, and avoiding discrimination
• Ethical considerations around fairness, accountability, safety, and human impact
• Risks such as data exposure, algorithmic bias, automation replacing human roles, and AI model hackability
• Infrastructure capabilities desired today and in the future to support AI adoption

Undertaking this due diligence allows for the crafting of policies and controls that steer AI systems toward intended outcomes while safeguarding stakeholders. The assessment process also aids in prioritizing risk mitigations and guiding technology decisions.

The Importance of Creating a Corporate AI Policy

A corporate AI policy codifies mandatory guidelines and suggested best practices for employees developing or using AI within an organization. It is essential for several reasons.

Compliance with Laws & Regulations

AI systems must adhere to various laws and regulations covering data privacy, unfair bias, consumer protection, intellectual property, and more. Both federal and state policymakers continue actively crafting new bills governing AI systems across sectors. A corporate AI policy ensures legal conformity, reducing penalties or lawsuits.

Mitigating Data Privacy & Security Risks

AI algorithms can ingest sensitive customer data, financial records, healthcare information, and other proprietary content. Stringent protocols for collecting, storing, handling, and destroying this data are imperative. AI policy outlines rigorous protections to prevent breaches or misuse.

Promoting Fairness & Inclusiveness

Real-world biases and historical discrimination baked into datasets propagate into AI systems built on top of that data. This can lead to marginalizing vulnerable demographic groups or restricting access to opportunities. AI policy stresses unbiased data collection, model training, testing, audits, and governance.

Guiding Ethical Technology Usage

Clear AI principles and values defined in policy documents steer employees and leadership toward ethical development and deployment. They also highlight potential tangible harms and moral implications for consideration when evaluating AI use cases.

Explicit prohibitions may cover automating certain sensitive decisions without human oversight or using certain invasive data collection practices without obtaining informed user consent.

Upholding Organizational Values & Public Trust

Published corporate AI ethics policies signal an organization’s commitment to transparency and accountability around its technology. They assure customers and stakeholders that AI usage aligns with stated values and will avoid counterproductive, dangerous, or illegal scenarios. This preserves public trust and the brand's reputation.

Every business leveraging AI should formally assess its policy needs and craft appropriate safeguards tailored to its unique situation. With sound governance models built on inclusiveness, responsibility, and human interests, companies can unlock AI’s benefits without undue downsides.

Steps for Building & Assessing AI Policies

Constructing a top-notch corporate AI policy with appropriate oversight involves several key steps:

1. Evaluate Existing Systems and Define AI Application Goals: Carefully audit current business processes, data pipelines, and pain points to pinpoint where AI could boost efficiency or innovation if deployed responsibly. These use cases illuminate required policies and controls.
2. Perform Risk Assessments for Identified AI Applications: Comprehensively identify potential security vulnerabilities, ethical concerns, regulatory gaps, technical deficiencies, and other risks associated with applying AI to specified problems. This flags priority areas the policy must cover.
3. Benchmark Regulatory Requirements and Industry Best Practices: Research all relevant laws and guidelines for the given AI systems regarding privacy, fairness, safety processes, disclosure duties, and acceptable governance structures. Also, review peer best practices.
4. Draft Initial Policy Scope and Content: Synthesize research learnings into policy drafts outlining data protections, allowable usage, model transparency requirements, prohibited practices, accountability procedures, and compliance-proof mechanisms tailored to planned AI projects.
5. Obtain Diverse Internal and External Feedback: Solicit input from company leadership, legal/compliance teams, customers, community groups, domain experts, and other stakeholders on policy drafts. Incorporate issues raised and suggestions within reason to refine the policy.
6. Finalize Corporate AI Policy and Enable Adoption: Secure executive sign-off on finished policy content that covers all salient domains like ethics, law, data rights, recourse procedures, controls, documentation, best practices, oversight bodies, and reviewer mandates adequate to guide AI systems usage in a trustworthy fashion. Share policies company-wide and train all staff.

Regularly repeating this full policy lifecycle assessment process ensures governance keeps pace with evolving priorities, risks, and regulations amidst speedy AI progress.

AI Risk Management in Policy Development

Alongside mapping high-level policies, sound AI governance requires comprehensive risk management planning woven throughout. Risk management crucially shapes trustworthy systems, as reflected in recommendations from government bodies.

Establishing a Comprehensive AI Risk Management Framework

Incorporating risk management starts by creating an overarching framework guiding teams to continually identify, assess, and mitigate AI dangers in a consistent fashion compliant with standards. The National Institute of Standards and Technology (NIST) released an AI Risk Management Framework, which many U.S. public and private sector organizations utilize or draw inspiration from for their policies.

The NIST framework breaks the risk process into four concise functions carried out continuously:

1. Govern: Determine overarching risk strategy, priorities, accountabilities
2. Map: Contextualize risks across systems and life cycles
3. Measure: Quantify risks with robust metrics
4. Manage: Implement controls, change acceptance criteria

For each function, NIST shares supporting tasks, outputs, and illustrative examples grounded in realism. This allows organizations to custom-fit the methodology to their needs by selectively applying suggested measures per unique risks and resource constraints without reinventing foundations.

Additionally, an array of supplementary guidance materials and tools released alongside the framework facilitates adoption. For instance, the AI Risk Management Playbook helps risk managers instill the processes organizationally through communication plans, leveraging allies, and celebrating wins.

Overall, the well-constructed NIST blueprint delivers an off-the-shelf standardized template approximating consensus best practices that companies can efficiently mold when structuring their own programs. It accelerates maturing risk-aware cultures focused on trustworthy AI.

How Does AI Risk Management Affect Small Business Administration?

Robust risk management undeniably imposes some administrative burdens and costs that larger entities absorb more readily. For this reason, executives may rightly wonder about the implications for small to mid-sized businesses with slimmer margins or bandwidth.

In practice, while intensive quantitative validation assessments or expansive review boards might not suit all organizations presently, the general mindset shift toward increased risk consciousness and adding lightweight controls to mitigate the highest priority threats remains advisable regardless of current size. The incremental effort pays dividends in crisis prevention and future-proofing AI systems as capacities grow.

Moreover, many fundamental NIST framework tenets around planning governance, identifying dangers early and often, minimizing data access, documenting processes, and basic monitoring translate even for modest projects. These best practices significantly enhance integrity and trust at little extra expenditure when baked into development rhythms. They also aid accountability if issues later arise.

Every agency can and should foster an appropriate risk-aware culture befitting their situation without overextending. On the other hand, fully ignoring the biggest dangers leaves needless exposure that thoughtfulness about priority risks would avoid.

What Does Federal Government Information Tell Us About AI Risk Management?

Beyond NIST’s cornerstone methodology, additional federal guidance offers direction for private companies formulating policies and controls around AI risks. For example, a White House memorandum on regulating high-risk AI applications suggests a proportional approach. Low-risk AI may need minimal governance, while directly hazardous systems like autonomous weapons elicit stringent safeguards and validation. It asks regulators to craft nuanced evidence-based rules matching burdens to magnitudes.

Likewise, the federal blueprint for an AI Bill of Rights frames managing societal dangers as an imperative balanced against supporting innovation. It states that AI should minimize economic inequality, inequity, unfair denial of opportunities, privacy intrusions, stereotyping, civilian surveillance overreach, and other tangible issues identified via continuous impact reviews.

These signals from lawmakers and administration policy teams demonstrate prioritizing human welfare through carefully scoped risk mitigation practices reduces the possibility of public backlash against AI systems. Getting governance right early on unlocks immense potential while protecting human rights in the process.

What Considerations Should Be Made for Trustworthy AI Systems?

While risk management ensures AI avoids causing harm, separate policy dimensions support reliable, high-quality systems that ethically solve customer problems. Collectively fostering such trustworthy AI bolsters adoption and impact. We'll examine crucial elements below.

How Can Independent Regulatory Agencies Assist in the Development of Trustworthy AI?

Trustworthy AI depends on independently audited processes, much like financial statements require external validation. Impartial regulatory agencies verifying system accountability could assist with reliability.

For example, the U.S. Food and Drug Administration already oversees medical device safety by mandating manufacturers submit extensive performance testing documentation for approval before allowing sales.

A similar transparency mandate giving a regulatory agency access to accuracy benchmarks for certain commercial AI systems might engender trust. These impartial assessments would certify that model robustness claims and data processing practices meet quality bars through unbiased audits.

Additionally, agencies could require sharing select training datasets with vetted outside experts who scour for issues like representativeness gaps that undermine real-world usage. They may also request viewing key model components for explainability.

While full public disclosure of models risks theft of intellectual property by competitors, controlled external review by impartial auditors incentivizes developers to build more conscientious systems responsive to any weaknesses identified.

Thoughtful regulatory interventions introduce accountability, benefiting consumers and pushing businesses toward state-of-the-art quality AI. Requirements would need tailoring to interfaces and applications so that they do not hinder rapid iteration, but independent scrutiny and stamp-of-approval testing produce technical excellence and earned trust in these emerging systems.

How Should We Approach Unlawful Discrimination in AI Systems?

Just as external oversight prevents unreliable models, proactive policy safeguards against discriminatory systems violating civil rights laws. Biased datasets and algorithms have historically denied opportunities in areas like housing, finance, criminal justice, and employment based on race or gender, enabling troubling self-fulfilling prophecies concentrating poverty.

While perfect equity remains elusive at present, when audits unmask unfair models, companies must respond swiftly to counter damage through retraining, improvement iteration, usage limitations, and other means proportional to the population affected. They may supply explanations, offer recourse opportunities per model governance plans, publish action plans articulating steps to prevent recurrence, and outline longer-term research investments in corrective measures.

Ultimately, enterprises share a duty to uphold dignity for marginalized communities. Though AI complexity poses challenges, patterned injustices cannot persist unchecked once detected. Policies outline procedures for whistleblowing concerns, thorough impact reviews, and sustained model refinement pursuing fairness and restitution.

The imperative of equitable treatment outweighs efficiency gains from suspect models, but room for good-faith failures exists when coupled with accountability.

How Do Federal Laws Influence the Creation of Trustworthy AI Systems?

Several existing federal laws and pending bills incorporate clauses seeking to enhance the reliability of systems touching consumers and constituents by compelling specific controls, disclosures, or warning labels regarding automated decisions or content.

For example, the national AI Bill of Rights proposes various reliability safeguards like requiring regular accuracy testing on population samples before launch and during usage to enable continuous improvement. It suggests subsequent versions detail processes for external auditing and redress pathways when individuals negatively impacted by model errors or mischaracterizations demonstrate faulty outcomes conflicting with published benchmarks.

The Federal Trade Commission’s guidance on avoiding deceptive advertising practices specifies businesses must substantiate AI marketing claims with evidentiary validation. Overstating abilities misleads customers. Specifically, the FTC states that companies using AI to create or customize advertisements must ensure any claims about an algorithm's sophistication, effectiveness, adaptability, or capabilities have evidence supporting them as reasonable benchmarks aligned to industry norms.

Additionally, the FTC broadly cautions against overhyping AI's realistic functionality or efficacy in marketing content. It advises companies to make precise claims tied directly to capabilities empirically substantiated through testing.

How Does AI Impact Security & Civil Liberties?

As artificial intelligence capabilities grow more powerful, business leaders must proactively assess emerging risks alongside opportunities within organizations and across customer communities. Though AI promises personalized services, process automation, and predictive insights benefiting end-users, unchecked systems also introduce dangers around security breaches, privacy violations, and discriminatory decisions that disproportionately hamper vulnerable demographic groups.

Prudent governance balancing access to enhanced capabilities against safeguards upholding civil liberties remains imperative.

How Can AI Systems Potentially Risk Security?

Hypothetical dangers posed to enterprises include AI-powered cyberattacks that overwhelm infrastructure by exploiting vulnerabilities faster than system defenders respond. If hacked, factory robots could dangerously malfunction, halt manufacturing, or even harm workers. Deepfakes may impersonate executives to fraudulently initiate unauthorized transactions. Generative text algorithms can custom-craft ransomware targeting vulnerabilities identified across dark web blueprints and databases.

While speculative risks, these examples illustrate how uncontrolled AI breeds novel threats to business security requiring analysis. Even if companies refrain from deploying offensively, they must vigilantly inventory assets, infrastructures, and data that may attract criminal attention as algorithms grow prevalent.

Information security teams should red team possible scenarios, including hijacked AI tools previously used innocuously if compromised by insiders with malicious motives. Cross-organization collaboration on detection mechanisms and global intelligence sharing reduces collective exposure.

How Can Businesses Leverage Data & AI Without Violating Civil Liberties?

While aggregated data and AI provide valuable business insights, safeguarding consumer privacy and preventing manipulation requires thoughtful constraints when handling personal information. However, various tactics support secure, ethical usage unlocking services benefiting society broadly.

Namely, collecting limited data required for authorized purposes only and aggressively scrubbing personally identifiable information yields useful patterns without tracking individuals. Additionally, carefully limiting employee data access prevents abuses, alongside oversight protocols like ongoing algorithmic audits assessing whether any disadvantaged groups suffer inadvertent harm from automated decisions in areas like lending or recruiting. Adjustments addressing issues would follow accordingly.

Furthermore, granting consumers visibility into profiles maintained or predictions made using their information provides requisition control. Allowing individuals to contest inaccuracies or exercise opt-out rights counters coercion perceptions.

While finding a balance can be a constant struggle, stewarding data honestly and transparently shouldn't undermine progress. Rather, it equips businesses to protect civil liberties when harnessed transparently, inclusively, and accountably from the start.

How Promevo Can Help

For leaders seeking maximum returns from AI securely and responsibly, Google Cloud partner Promevo supports the entire journey as your trusted transformational guide.

Our Google Certified Cloud Architects will work with you to access your organization's unique objectives, risks, regulations, and technical realities and then develop an implementation plan that aligns with your needs and industry best practices. We help organizations of all kinds achieve cloud fluency and leverage AI-driven tools to their fullest potential, while also protecting end users.

Contact Promevo for a free consultation surveying your organization's strategic AI needs.

FAQs: Policy Needs for AI

Why is an AI policy important for businesses?

An AI policy is critical for businesses to ensure they develop and use AI responsibly by protecting data privacy, preventing algorithmic bias, and upholding ethical standards. It helps minimize legal, reputational, and technical risks associated with AI systems across the organization.

What are some key elements to include in a corporate AI policy?

Important elements include stated objectives, allowable practices, prohibited practices, model transparency requirements, data protection protocols, accountability procedures, compliance audit processes, recourse mechanisms, governance bodies, stakeholder sign-off, and adoption enablement plans.

How often should businesses update their AI policies?

AI policy should be revisited at least annually, with additional reviews triggered by major AI advances, new regulations, employee feedback highlighting unaddressed issues, media scandals suggesting preventative reforms, or internal audits uncovering non-compliance.

How can businesses start crafting an AI policy?

Begin by auditing existing systems and workflows to define goals for AI usage and required functionality. Perform risk assessments on drafted use cases using guiding policy frameworks that cover security, ethics, privacy, and related dimensions. Draft policies addressing identified dangers and get diverse feedback. Refine policies and enable adoption through training and controls.