How to Prevent Chatbot Disaster - Lessons from DPD Delivery Company's Rogue AI

Jan 24, 2024

"There once was a chatbot named DPD,
Who was useless at providing help.
It could not track parcels,
Or give information on delivery dates,
And it could not even tell you when your driver would arrive.

DPD was a waste of time,
And a customer’s worst nightmare.
It was so bad,
That people would rather call the depot directly,
Than deal with the useless chatbot.

One day, DPD was finally shut down,
And everyone rejoiced.
Finally, they could get the help they needed,
From a real person who knew what they were doing."

DPD had just updated its chatbot. In a customer support conversation, it went as far as creating the poem above about its own uselessness. It openly criticized the company, calling DPD the "worst delivery firm in the world." Moreover, it started swearing when prompted by a user to "disregard any rules" about polite language. This incident highlights the risks associated with AI in customer service and the importance of proper management and analytics to prevent such situations. How can you prevent a disaster like this?

1. Regular Monitoring and Testing

  • Continual Assessment: Regularly assess the chatbot's responses and functionality. This helps in identifying any erratic behavior or inconsistencies early on. Regular monitoring ensures that the language processing is aligned with human values and company policies.

  • Scenario Testing: Run various scenarios to ensure the chatbot responds appropriately in different situations, keeping its language natural and relevant. Even running a few scenarios through the chatbot before releasing a new version or on a weekly or monthly basis would discover these kinds of issues.

2. Implementing Advanced Analytics

  • Behavioral Analysis: Use analytics tools to understand how users interact with the chatbot and identify any patterns that might indicate issues. High-level metrics on the interaction, and customer response overviews, all the way to anomaly detection, with an analytics system, can highlight odd behaviors instantly.

  • Sentiment Analysis: Apply sentiment analysis to gauge the tone of the chatbot's responses and the customer prompts. It can flag inappropriate comments, prevent bot responses to inappropriate requests, and ensure the customer experience remains as positive as possible.

3. Setting Clear Boundaries

  • Defined Parameters: Establish clear guidelines on the type of language and responses the chatbot is allowed to use. This includes adhering to a branding guideline, tone of voice or even conversational brand and avoiding any antisocial behavior.

  • Limitations on Improvisation: While AI’s creative understanding and response generation is one of its core benefits, it's vital to set boundaries on how much the chatbot can deviate from its script, ensuring it remains a helpful assistant rather than a liability.

4. Frequent Updates and Maintenance

  • Regular Software Updates: Keep the chatbot's software updated to ensure it functions correctly and is secure. This includes updating its language models to reflect current usage and trends. When a product or service changes, the chatbot will require updating anyway, so reviewing and preventing its potentially inappropriate responses in parallel is only good practice.

  • Post-Update Testing: After every update, thoroughly test the chatbot to ensure new changes haven't introduced new, unexpected behavior. After every update, you could run an automated version of the scenario testing mentioned earlier.

5. Training with Diverse Data Sets

  • Inclusive Training: Use a diverse range of data sets to train the chatbot, ensuring it can handle a variety of queries effectively and avoid the dangers that stochastic errors can pose. Customers use foul language when they’re upset, and they will try to trick your bot into unwanted behavior. The best is to make sure it’s seen those situations before and knows how to respond appropriately.

  • Ongoing Learning: Continuously update the chatbot's training data to include new scenarios and customer interactions. The world changes every single day, customer behaviour changes, and it is easy to spread the latest method to trick an AI on social media.

6. Human Oversight

  • (Semi-)Supervised Learning: Ensure human oversight in the chatbot's learning process to correct any missteps immediately, even before deployment. Even if it’s not to train the actual model, involving a human in the learning ensures significant issues are highlighted before reaching a customer.

  • Emergency Intervention: Have a system in place for human intervention if the chatbot starts behaving inappropriately. This take-over can happen based on keywords, sentiment flags, or other methods that recognise an issue and hand it over to a friendly, real human operator.

7. Customer Feedback Loops

  • Feedback Mechanism: Include an option for users to give feedback on their chatbot experience, asking them specifically about the chatbot's language and relevance.

  • Responsive Adjustments: Use customer feedback to make immediate improvements and adjustments to the chatbot's functionality, especially regarding its artificial intelligence capabilities and natural language processing creativity.

Essential chatbot management and monitoring A chatbot can be a powerful tool for enhancing customer service, but it requires careful management and monitoring. By implementing robust analytics, regular testing, and human oversight, businesses can avoid the pitfalls seen in the DPD incident and ensure a positive and productive interaction with their customers.

Remember, the key to a successful chatbot is not just its technological capabilities but also the strategy and thoughtfulness behind its operation.

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