AI For HOAs: Evaluating GenAI Solutions in Community Association Management

Let’s face it: everyone who is anyone has launched a GenAI solution within their product. After all, with GenAI being the next shift in the way we work since the Internet and dot com era, not utilizing GenAI feels almost archaic. But while the race to AI has resulted in incredible transformations – especially when it comes to AI for HOAs – there are many products and solutions that were built (or are being built) simply to stay relevant.

So how do you know the difference between a solution that will improve productivity and drive revenue, or one that is only going to negatively disrupt your business? Here are a few considerations to take into account when evaluating GenAI within a tech stack solution for community association management companies:

The Importance of Domain Specific Language Learning Models (LLMs)

When one is using an open source platform for general AI purposes – such as ChatGPT or CoPilot – they are using a general-purpose language model. A general purpose LLM supports a wide range of tasks and is great for writing an email or spell-checking a document. But when an executive wishes to use an LLM for a specific use case – such as managing collections-based communications – a domain specific LLM is more suitable. This is because the LLM is highly trained in specific domains and use cases, resulting in high accuracy and relevance.

When evaluating GenAI tools for a management company, one should consider the problem they are trying to solve. If the problem is very specific to a complex issue – such as improving accounts receivables recovery – they are going to achieve significantly better results using a solution that is specifically trained to address that issue.

Management company executives should also consider how long the organization has been in business, and how much training the LLM has had by the company’s development and product team. LLMs are only as good as the people who train them, so using a novice LLM trained by personnel who are not subject matter experts will lower accuracy and results.

Considering the Risks Behind Poorly Designed GenAI Models

When designed poorly, AI solutions pose key vulnerabilities that compromise integrity, accuracy, and security in their outputs. Integrity risks arise from attacks that cause AI systems to produce unintended results, which can be summarized into these four types:

1. Data Poisoning: Attackers tamper with training data to lower a model’s accuracy or make it produce specific errors. This is particularly problematic in shared or public datasets where quality control is challenging.
2. Evasion and Misdirection Attacks: Hackers manipulate inputs to fool AI into giving wrong answers, like misidentifying images or objects. These exploits highlight weaknesses in how AI processes data.
3. GenAI Hallucinations: If one has ever laughed at the complete inaccuracy of an AI-generated answer or image, they have GenAI hallucinations to thank for that. Generative AI can create false or inaccurate information, known as “hallucinations,” due to its reliance on statistical predictions and lack of deeper understanding. This can lead to incorrect answers that may at best frustrate a consumer, but at worst, impose costly consequences to the management company for providing inaccurate information to the homeowner or Board Member.
4. Reasoning Failures: AI models struggle with reasoning and planning, often producing shallow or flawed results because they rely on statistical guesses rather than true logic.

AI models that are produced effectively combat risk integrity to provide the most accurate information to the client. Here’s how: 

1. Uncertainty Quantification (UQ): UQ identifies and measures uncertainties in ML models, including random data variability (aleatoric) and gaps in model knowledge (epistemic). 
2. Retrieval Augmented Generation (RAG): RAG uses search algorithms to query external data, such as web pages, knowledge bases, and databases. It then incorporates the pre-processed retrieved information into the pre-trained language learning model (LLM.) This helps generate more accurate and up-to-date information.
3. Representation Engineering: This method analyzes models to uncover and fix problems like biases and hallucinations. While typically requiring internal model access, new techniques are improving reliability even in black-box systems.

While UQ, RAG, and Representation Engineering improve accuracy supplied by GenAI solutions, they are still prone to issues when the data is tampered by hackers. This is why a secure platform is essential, which we will discuss in the next section.

Keeping Homeowner Data Confidential

Management companies hold some of the most sensitive data in the United States, and keeping this data safe is essential to the homeowner and the organization’s reputation. The most important thing a management company executive can do to safeguard their homeowners’ data is to use a secure software solution that employs sophisticated standards to ward off attacks and prevent any data compromise. Software providers will strong secure infrastructures will naturally ensure any integration partner is also secure.

When it comes to GenAI, a general understanding of the risks and solutions is important to evaluate solutions appropriately. Some of the key ways in which GenAI can obstruct data privacy include:

1. Jailbreaking and Transfer Attacks: Techniques like prompt injection can bypass safety guardrails in LLMs, enabling harmful outputs.
2. Model Inversion and Membership Inference: Attackers can extract sensitive training data, such as survey responses or health information, by querying models. This poses significant privacy concerns, especially when applied to expensive, proprietary models.
3. LLM Memorization: Models may overfit during training, memorizing and reproducing sensitive or copyrighted data instead of generating novel outputs. This can lead to privacy breaches and intellectual property violations.
4. Black-Box Search: Adversaries exploit APIs to generate adversarial prompts that bypass safeguards, revealing sensitive information. Leakage prompts can extract confidence scores and facilitate further attacks, such as model inversion.

To balance security and accuracy, proper AI-designed solutions include the following procedures:

1. Differential Privacy: Differential privacy is a mathematical approach to protect individual data during analysis. It allows for sharing insights about groups while safeguarding personal privacy. By adding random noise to results, it ensures outputs remain consistent regardless of whether an individual’s data is included, making it resistant to attacks like re-identification or record linkage. This method enables secure data analysis without compromising individual privacy. Large tech companies – including Apple, Meta, and Google – are reliant on differential privacy to mitigate privacy concerns. 

2. Unlearning Techniques: Unlearning techniques in AI allow models to selectively remove the influence of specific data points without requiring complete retraining. By “forgetting” certain information, unlearning ensures that models remain functional and relevant while adhering to ethical and regulatory standards. Unlearning techniques are crucial for HOAs using AI tools because they ensure compliance with privacy regulations, maintain fairness in decision-making, and adapt to changing community needs. 

Deploying GenAI Responsibily

Building on earlier discussions of risks related to integrity and confidentiality in GenAI systems, governance challenges highlight the need for robust frameworks to address issues like accountability, cybersecurity, and operational transparency. As AI adoption grows, so does the responsibility to ensure these technologies are not only effective but also ethical and secure. Here are a few challenges AI platforms may face: 

1. Deepfakes: Generative AI can create fake content across multiple formats like text, images, and videos, making it harder to distinguish real from fake. AI is both a tool for creating deepfakes and detecting them, fueling a continuous battle between disinformation and detection.
2. Overfitting: When AI models are trained too specifically on their training data, they may perform poorly on new data, leading to errors. This happens when models fail to generalize properly, making overfitting a governance and quality risk.
3. Bias: AI systems often inherent biases from training data that may not align with their operational use, such as gender or cultural biases. Correcting this is challenging due to limited availability of unbiased data and the difficulty of aligning training processes with real-world applications.
4. Toxic Text: Generative AI can produce harmful or offensive content because its training data often includes both good and bad sources from the internet. Filtering techniques, though improving, still fail to fully eliminate toxicity in AI outputs.

Proper AI design would resolve these concerns via the following strategies:

1. Advanced Detection and Verification: This strategy implements robust mechanisms, such as watermarking and cross-verification against trusted data sources, to combat deepfake creation and ensure content authenticity.
2. Comprehensive Training Practices: Through this practice, development teams use diverse datasets and cross-validation techniques to prevent overfitting and bias, ensuring models generalize effectively and fairly across real-world applications.
3. Ethical Oversight and Auditing: Teams employing effective GenAI should conduct regular bias audits and adhere to ethical guidelines to minimize discriminatory impacts and align AI systems with fairness and inclusivity standards.
4. Enhanced Filtering and Feedback Loops: Finally, it is important to leverage advanced content filtering and reinforcement learning with human feedback (RLHF) to mitigate harmful or toxic outputs while continuously improving model behavior.

AI for HOAs: How Do You Know What’s Best?

When evaluating GenAI solutions in community association management, one should consider the following:

1. Make sure the LLM is built specifically for the solution that you are trying to solve. Ensure that the LLM is trained for that specific use case for best accuracy.
2. Ask about the design of the solution. Ensure that the partner is using proper techniques to adequately train the LLM and prevent challenges such as AI hallucinations.
3. Keep security top of mind. Be sure that the solution will protect homeowner data and finances.
4. Finally, get to know the people behind the product. As we’ve said before, the AI is only as good as the people behind the AI. Make sure that the solution is developed from a trusted source and a subject matter expert.

TechCollect stands out as a trusted solution in the world of GenAI, addressing the challenges and needs outlined in this blog with precision and expertise. Unlike general-purpose AI platforms, TechCollect is specifically trained for accounts receivables recovery, leveraging over 20 years of industry experience to provide unparalleled accuracy, security, and functionality. Our AI is designed to tackle the unique complexities of community association management, ensuring compliance with state regulations, reducing labor, and improving recovery rates.

If you’re ready to see how a purpose-built GenAI solution can transform your operations and drive measurable results, TechCollect is here to help. Reach out today to schedule a demo and discover why we are the trusted choice for AR recovery and community management solutions.