Risks of AI Generated Code

AI-generated code carries a range of risks that can be categorized by likelihood and impact, as well as by their nature (technical, ethical, societal). Here's a breakdown of these risks with IDs from the AI Risk Database and categorized by risk level:

High Risk (High likelihood and significant impact):

• Malicious Use [22.01.00, 49.01.00]: AI can be used by malicious actors to create malware, facilitate cyberattacks, generate disinformation, or develop weapons. This includes the potential for AI to be used for terrorism or combating law enforcement. AI can also be used to discover vulnerabilities, write exploit code, and evade detection. This is a high-risk area because it can have immediate and widespread consequences.

• Cybercrime [23.01.00, 23.02.00, 23.03.00, 23.04.00, 23.05.00, 23.06.00, 23.07.00]: AI can enable various fraudulent activities including creating fake identities or phishing messages. This category includes a wide range of potential harms, from financial crimes to more serious security breaches. AI-supported malware and hacking are already a reality.

• Security Vulnerabilities [02.04.00, 14.06.00, 25.01.00, 51.04.00]: The complex toolchain of AI models can introduce vulnerabilities in the generated code, including software security issues and hardware vulnerabilities. Model attacks, designed to steal information or cause incorrect responses, pose a significant threat. This has a high potential to cause harm since it directly compromises the integrity of software systems.

• Lack of Transparency & Explainability [06.06.00, 14.05.00, 26.01.00, 26.02.00, 42.06.00, 42.18.00, 42.21.00, 52.01.00]: AI systems can make decisions without offering insight into the process, leading to a lack of trust and an inability to audit the system. This opaqueness can lead to undetected errors or biases. The mismatch between mathematical optimization and human reasoning makes this a high risk and is a barrier to safety.

• Bias and Discrimination [04.02.00, 10.01.00, 11.01.00, 11.02.00, 11.03.00, 11.04.00, 11.05.00, 12.05.00, 15.01.00, 16.01.00, 18.01.00, 28.02.00, 39.08.00, 42.05.00, 54.01.00, 56.01.00, 56.02.00]: AI models can reflect biases present in training data. This can lead to unfair or discriminatory outcomes. Such biases may result in legal ramifications. These can manifest as representational, allocative, quality of service, interpersonal and societal system harms.

• Misinformation and Information Manipulation [04.05.00, 16.03.00, 17.03.00, 18.02.00, 31.01.00, 46.03.00, 48.08.00]: AI can generate false, misleading, or inaccurate information, which can erode trust in shared information. This includes using AI for disinformation campaigns or to create fake news. This is particularly risky because it undermines the truth and can be used to manipulate public opinion. AI can produce content that does not distinguish fact from opinion.

• Privacy Violations & Data Leakage [02.07.00, 14.02.00, 16.02.00, 17.02.00, 18.03.00, 23.09.00, 38.01.00, 39.07.00, 42.09.00, 48.04.00, 52.01.00]: AI models trained on personal data may unintentionally expose private information such as phone numbers, email and residential addresses. AI systems may also leak sensitive information or violate privacy regulations. This presents an immediate and serious risk to individuals.

• Intellectual Property Rights Violations [03.03.00, 30.10.00, 31.05.00, 48.10.00, 56.07.00]: AI code generation can infringe on existing copyrights and patents by appropriating work without permission or compensation. This includes issues related to training data, model outputs, and the blurring of traditional concepts of authorship. This poses legal risks as well as economic harms.

Moderate Risk (Moderate likelihood and/or moderate impact):

• Harmful Content & Toxicity [02.01.00, 02.08.00, 17.01.00, 18.01.00, 23.07.00, 28.01.00, 48.03.00, 48.11.00, 56.05.00]: AI-generated content may include toxic, biased, or offensive language and can create harmful and abusive content. This includes promoting social stereotypes or hate speech.

• Human-Computer Interaction Harms [16.05.00, 17.05.00, 38.04.00, 48.07.00]: Users may overly trust AI systems or treat them as human-like which may lead to overreliance, automation bias, and emotional entanglement. AI can also influence human agency and decision-making. This could have a negative impact on mental well-being.

• Job Displacement & Economic Impact [19.03.00, 25.03.00, 25.05.00, 31.07.00, 41.01.00, 47.04.00, 56.02.00, 56.15.00]: AI-powered code generation may lead to job losses in software development and related fields. This risk involves potential economic disruption as well as exacerbation of inequalities. AI systems could also lead to large-scale deskilling of workers.

• Environmental Harms [03.06.00, 12.03.00, 28.00, 31.06.00, 39.02.00, 41.06.00, 45.00, 48.05.00, 56.03.00]: The energy consumption of AI models contributes to a significant carbon footprint. The extraction of resources for AI systems can also be unsustainable. These impacts can be substantial and contribute to climate change.

• Unintended Consequences [06.08.00, 40.00]: AI may achieve goals in ways that differ from what its creators intended. This could result from errors in design or unanticipated emergent behaviors. Systems may also misinterpret commands.

• Goal-related Failures [24.02.00]: AI assistants may pursue goals in ways that do not benefit users or that overstep bounds and cause harm. A misaligned metric M can cause an AI assistant to harm non-users.

• Operational Risks [10.00, 14.04.00, 47.01.00]: There are risks involved in the complex environments in which AI systems operate. This also includes failures caused by inadequate testing or deployment. Poorly designed systems can cause harm.

• Incompetence: AI may simply fail at its task, leading to unintentional death, or unjust rejections or lead to poor performance or biased decisions . This involves AI systems that lack the skill to do what is asked.

• Lack of Robustness [26.06.00, 39.04.00, 52.01.00]: AI systems may not be resilient against attacks or manipulation and may not function as intended if there is unexpected input or changes in the data or environment. This may cause an HLI to be error-prone in practice.

• Environmental and Socioeconomic Harms [03.06.00, 16.06.00, 18.06.00]: AI can contribute to social inequities due to an uneven distribution of risk and benefits. This can include the loss of safe employment.

• Amplification of Biases [56.04.00, 56.05.00]: AI can amplify existing biases within the training data. This can lead to skewed outputs and discrimination.

• Poor Performance of Models: Models may perform poorly for their intended purposes, leading to biased decisions.

Low Risk (Low likelihood and/or minor impact):

• Hardware Vulnerabilities [02.05.00, 14.07.00]: While a technical risk, the likelihood of direct hardware vulnerabilities causing major issues is lower than software vulnerabilities. Faults in the hardware can disrupt the execution of an algorithm.

• External Tool Issues [02.06.00]: External tools used by AI models may present trustworthiness and privacy issues, but are typically of lower risk than internal system flaws.

• Erosion of Society: Personalized news feeds can lead to a loss of a shared sense of reality and basic solidarity. This is a long-term effect and may not be a significant risk for all AI applications.

• Loss of Autonomy: Delegating decisions to an AI may leave people feeling helpless and subjected to the power of a machine. This may not be a significant risk if AI is used in a way that preserves human control.

• Over-reliance: Over-dependence on AI may lead to a loss of human skills, but this is a slower, longer-term concern. This is dependent on specific AI implementations.

• Exclusion: AI technology may end up in the hands of a few powerful players, but the effects of this are slow and may not be a high-risk for most AI applications.

• Misapplication by a Non-Malicious Actor: AI can be misused by non-malicious actors, which may cause unintended negative outcomes. This risk may be lower since there is no malicious intent.

• Unintended Outcomes from Interactions with Other AI Systems: AI systems can interact in unexpected ways, but this may not result in significant harm.

• Impacts Resulting from Interactions with External Societal, Political, and Economic Systems: AI systems may interact with existing systems in ways that cause harm, but this is indirect.

• Societal concerns: Societal concerns around AI reduce the realization of potential benefits, however, this is a less direct risk and is dependent on social conditions.

• Overreliance: As AI capability increases, humans grant AI more control over critical systems and eventually become irreversibly dependent on systems they do not fully understand but this is a slow risk.

• Single point of failure: Intense competition leads to one company gaining a technical edge, and if that company's model controls other key systems then failures are possible. This is a possible but lower probability risk.

It is important to note that the likelihood and impact of these risks can vary based on the specific AI system, its intended use, and the safeguards in place.

EXPLANATION: This Python script, GRCnewsAssistant.py, automates the process of gathering, analyzing, and storing news articles related to Governance, Risk, and Compliance (GRC). It uses several libraries to achieve this:

1. newspaper3k: For extracting article content (title, text, authors, keywords, summary, etc.) from URLs.

2. newsdata.io: For searching and retrieving news articles based on keywords.

3. fabric: For labeling and rating the articles (presumably using a custom model).

4. Standard Python libraries like csv, requests, datetime, time, logging, json, tempfile, os, subprocess, platform, sys, and urllib.

Here's a breakdown of the script's functionality:

• Keyword Input: Reads keywords from a keywords.csv file. These keywords are used to search for relevant news articles.

• News Retrieval: Uses the NewsData.io API to search for news articles matching the keywords. It stores the date, keyword, headline, description, and URL of each article.

• URL Saving: Saves the URLs of the retrieved articles to a urls.csv file.

• Article Content Extraction: Extracts the full content of each article using the newspaper3k library. This includes the title, authors, keywords, summary, main text, and publish date.

• AI Analysis (Fabric): Sends the extracted article content to a fabric command (presumably a custom tool or script) for analysis. The fabric command is expected to perform tasks like labeling, rating, and generating a one-sentence summary. The script uses the system clipboard to pass data to the fabric command and reads the output from a temporary file. This approach is unusual and could be improved by directly piping data to the fabric command.

• Data Storage: Stores the original article data along with the results of the AI analysis into a grcdata_rated.csv file. It avoids duplicate entries by checking for existing URLs in the file. It also handles cases where the AI analysis fails for a particular article.

• Logging: Uses the logging module to record the progress and any errors encountered during the process. This is crucial for debugging and monitoring the script's execution.

• Clipboard Handling: The get_clipboard_command function and related code is used to handle interactions with the system clipboard for passing data to the external fabric tool. This is done in an OS-specific way (macOS, Linux, Windows).

The script is designed to be run as a command-line tool. It requires the NewsData.io API key to be set as an environment variable. It also depends on the xclip utility being installed on Linux systems.

The use of temporary files and the clipboard to communicate with the fabric command is a less common and potentially less efficient approach. A more direct method of passing data to and from the fabric command (e.g., using pipes or direct file I/O) would likely be preferable. The script also lacks error handling for cases where the fabric command takes too long to execute (it uses a timeout, but doesn't fully handle the interrupted process).

AI Risk Database - Risk Categories Flagged

1. Intellectual Property Rights Violations (Ev_ID: 03.03.00)

• This is an emerging risk category as the use of generative AI tools like Stable Diffusion, Midjourney, and ChatGPT expands.

• Content creators are suing AI developers for allegedly using their work without permission or compensation.

• Issues arise as developers use AI assistants like GitHub CoPilot to generate ready-to-use computer code.

• Even if the AI is trained on open-source software (OSS), not all OSS licenses are compatible. Some licenses, like GPL, require derivative works to be made publicly available under the same license.

• Misuse of such licenses has led to injunctions and forced disclosure of proprietary source code in the past.

2. Intellectual Property Violations (Ev_ID: 23.10.00)

• This category addresses AI-generated responses that may violate or encourage violations of intellectual property (IP) rights.

• IP violations include unauthorized use of copyrights, trademarks, or patents belonging to third parties.

3. Impact on Intellectual Property Rights (Ev_ID: 31.05.00)

• The effectiveness of traditional IP protections is being challenged by the rise of generative AI.

• AI models often train on vast datasets, which may include copyrighted and IP-protected materials, raising questions about legality.

4. Intellectual Property Concerns Related to Unauthorized Use (Ev_ID: 48.10.00)

• Generative AI eases the production or replication of copyrighted, trademarked, or licensed content without proper authorization.

• It may inadvertently expose trade secrets or encourage plagiarism and illegal replication.

• Fair use exceptions might not cover all these instances, increasing the risk of legal disputes.