# Lifelong Personal Context Recognition

arXiv: https://arxiv.org/abs/2205.10123 · HTML: https://mlsanity.com/paper/2205.10123/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 10 May 2022.

## Builds on (most cited first, 12 of 12)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Interactive Label Cleaning with Example-based Explanations](https://mlsanity.com/paper/2106.03922/) (2021)
- [Learning in the Wild with Incremental Skeptical Gaussian Processes](https://mlsanity.com/paper/2011.00928/) (2020)
- [Continual egocentric object recognition](https://mlsanity.com/paper/1912.05029/) (2019)
- [Towards Visual Semantics](https://mlsanity.com/paper/2104.12379/) (2021)
- [Human-in-the-loop Handling of Knowledge Drift](https://mlsanity.com/paper/2103.14874/) (2021)
- [Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs](https://mlsanity.com/paper/2202.08566/) (2022)
- [Improving time use measurement with personal big data collection -- the experience of the European Big Data Hackathon 2019](https://mlsanity.com/paper/2004.11940/) (2020)
- [Multi-Modal Subjective Context Modelling and Recognition](https://mlsanity.com/paper/2011.09671/) (2020)
- [Streaming and Learning the Personal Context](https://mlsanity.com/paper/2108.08234/) (2021)

## Built on by (most cited first, 2 of 2)

- [Factors Impacting the Quality of User Answers on Smartphones](https://mlsanity.com/paper/2306.00627/) (2023)
- [Help the machine to help you: an evaluation in the wild of egocentric data cleaning via skeptical learning](https://mlsanity.com/paper/2510.23635/) (2025)

## Abstract (arXiv)

We focus on the development of AIs which live in lifelong symbiosis with a human. The key prerequisite for this task is that the AI understands - at any moment in time - the personal situational context that the human is in. We outline the key challenges that this task brings forth, namely (i) handling the human-like and ego-centric nature of the the user's context, necessary for understanding and providing useful suggestions, (ii) performing lifelong context recognition using machine learning in a way that is robust to change, and (iii) maintaining alignment between the AI's and human's representations of the world through continual bidirectional interaction. In this short paper, we summarize our recent attempts at tackling these challenges, discuss the lessons learned, and highlight directions of future research. The main take-away message is that pursuing this project requires research which lies at the intersection of knowledge representation and machine learning. Neither technology can achieve this goal without the other.