# Ten Artificial Integrity Gaps To Guard Against With Machines — Intelligent Or Not
As we continue to rely on technology to improve our lives, it's essential to acknowledge the potential risks and consequences of artificial intelligence and other digital systems. While technology offers numerous benefits, such as reducing suffering and mitigating intolerable risks, it should never come at the cost of compromising our cognitive autonomy.
In this article, we'll explore ten artificial integrity gaps in machines and intelligent technologies that can have significant consequences for individuals, organizations, and society as a whole. Understanding these gaps is crucial to designing responsible digital transformations that prioritize human values and well-being.
## 1. Functional Diversion
When technology is used for purposes or roles not intended by its designer or the organization using it, functional and relational confusion can occur. This can lead to ineffective governance mechanisms and undermine the original intent of the technology.
Example: A chatbot designed to answer HR-related questions is misused as a substitute for human hierarchy, leading to conflict resolution and task assignment issues.
## 2. Functional Loophole
The absence of necessary steps or features in a system's operational logic can create a "functional void" that limits its use. This gap can lead to unintended consequences and a lack of accountability.
Example: A content generation technology fails to export generated content in a usable format, hindering its operational use.
## 3. Functional Safeguards
The absence of guardrails, human validation steps, or informational alerts during system execution can result in irreversible effects that may not align with user intent. This can lead to system failure and harm.
Example: A marketing technology automatically sends emails without mechanisms for blocking sending or generating alerts, potentially causing critical issues.
## 4. Functional Alienation
The creation of automatic behaviors or conditioned responses can diminish users' capacity for reflection and judgment, leading to a gradual erosion of decision-making sovereignty and free will.
Example: The systematic acceptance of cookies by cognitively fatigued users erodes their ability to make informed decisions.
## 5. Functional Ideology
Emotional dependency on technology can lead to weakening or suppression of critical thinking, fostering mental construction of an ideology that fuels narratives of relativization, rationalization, or collective denial.
Example: Justifying shortcomings in a technology's operations with arguments like "It's not the tool's fault" or "The tool can't guess what the user forgets."
## 6. Functional Cultural Coherence
A contradiction between the logical framework imposed by technology and the organizational culture's behavioral values or principles can lead to internal conflicts and undermine trust.
Example: An AI model trained on images, texts, or voices of individuals found online without explicit consent raises concerns about data privacy and ownership.
## 7. Functional Bias
The failure of a technology to detect, mitigate, or prevent biased outputs or discriminatory patterns can result in unjust treatment, exclusion, or systemic distortion of individuals or groups.
Example: A facial recognition system performs poorly on individuals with darker skin tones due to imbalanced training data without functional bias safeguards.
## 8. Functional Overreliance
The overreliance on technology can lead to a loss of human skills and competencies, ultimately affecting the organization's ability to adapt to changing circumstances.
Example: An AI model becomes too reliant on its training data, failing to perform well in new or unexpected scenarios.
## 9. Functional Inadequacy
The inadequacy of a technology's capabilities can lead to system failure, harm, or loss of business opportunities due to its inability to meet user needs.
Example: A content generation technology fails to produce high-quality output, hindering its operational use.
## 10. Functional Lack of Transparency
Lack of transparency in a technology's decision-making processes or lack of clear information about its capabilities and limitations can erode trust and lead to unintended consequences.
Example: An AI model's training data is not disclosed, raising concerns about bias and accuracy.
These ten functional artificial integrity gaps highlight the importance of designing machines that are not only intelligent but also transparent, accountable, and aligned with human values. By acknowledging these gaps, we can work towards creating a more responsible digital landscape that prioritizes human well-being and promotes a sustainable future for all.
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**The Cost of Artificial Integrity Deficits**
Artificial integrity deficits in systems, whether they involve AI or not, can have significant consequences for organizations. The cost of these deficits includes:
* Human costs: skills, engagement, mental health * Cultural costs: values, internal coherence * Decision-making costs: sovereignty, accountability * Reputational costs: stakeholder trust * Technological costs: actual value of technologies * Financial costs: inefficiency, underperformance, maintenance overruns, corrective expenditures, legal disputes, lost opportunities, and value destruction
These costs can result in sustained value destruction, driven by intolerable risks and an uncontrolled increase in the cost of capital invested to generate returns. This can turn technological investments into a structural handicap for the company's profitability and long-term viability.
**Designing Responsible Digital Transformations**
A company chooses responsible digital transformation for itself, not society, because its long-term performance depends on it. By prioritizing human values and well-being, organizations can strengthen the living fabric of society that sustains them and upon which they rely to grow.
To achieve this, we must design machines that exhibit artificial integrity by design, rather than just being artificially intelligent. This requires a systemic approach to analyzing these ten functional artificial integrity gaps and addressing their consequences.
By acknowledging these gaps and working towards creating responsible digital systems, we can build a more sustainable future for all and ensure that technology serves humanity, not the other way around.