Automated Processing Statement

1. Why this statement exists

ScreenJournal analyses the screen activity of the people it monitors and draws conclusions about it: how productive a stretch of work looked, how a week compares with colleagues', whether activity may have been simulated, and whether a rule the employer wrote was met. This statement explains what is inferred, from what, by what means, where it goes wrong, and how a person can have it checked by a human and contest it. It is written for employers deciding how to use the service, for the employees it monitors, and for procurement and compliance teams assessing it.

2. What is inferred

The service draws five kinds of automated inference about the people it monitors:

1. an activity score from 1 to 5 for each recorded segment of screen activity;
2. a productivity percentage, measured against the working day;
3. a position in a weekly ranking of the organisation's members;
4. flags that activity may be simulated, for example by a mouse-jiggling device;
5. matches against alert rules written by the organisation.

Segment scores and descriptions are produced by a Google Gemini model accessed through Google Vertex AI, and alert evaluations by a Google Gemini model accessed through Google's Gemini API, from screen content and activity signals such as presence and idle time. The productivity percentage and the weekly ranking are calculated from those scores. Simulated-activity flags come from the same model reviewing the segment's screen recording, on which the desktop app marks the cursor position and clicks; fixed checks in our code then discount the device's own idle periods and apply minimum thresholds before a flag is set. These outputs are probabilistic. They can be wrong, and they describe what was on screen, not the quality or value of anyone's work.

ScreenJournal makes no employment decision. Any decision about a person, whether on pay, performance, discipline or anything else, is made by your employer's managers, who are responsible for it.

The following human-review surfaces exist today:

• the Review page, where managers can examine flagged segments, and the alerts inbox, where a manager approves or rejects a member's explanation;
• corrections, where the organisation's administrators change a segment's score;
• Add a reason, where a member, or a manager on the member's timeline, explains a paused, offline or away stretch (when the organisation's policy allows manual entries);
• the member's own Activity timeline, which shows their segments, descriptions and scores.

If you disagree with a score, flag or ranking, ask your employer first. Your employer can correct the record or redact a time range. If your employer does not respond, contact support@screenjournal.ai.

A flag that activity may be simulated is an indicator that calls for human judgement. It is never proof of misconduct, and it should not be relied on without a person reviewing the underlying activity.

3. What the model sees, and what it never sees

The model receives:

• screen content from the applications and sites the employer allows, as short video segments of each connected display, with the cursor position and clicks painted into the recording as markers by the desktop app;
• window titles, application names and browser addresses;
• activity signals: whether the device was in use or idle, and presence such as paused, locked or offline time;
• audio, only where the employer enables it: calls and meetings are transcribed by the same family of model, and a transcript can be checked against the employer's suppression rules.

The model never receives the content of keystrokes, anything from applications or sites on the employer's exclusion list, or anything while the person is signed out or tracking is stopped.

4. How each inference is produced

• Segment scores and descriptions are written by a Google Gemini model through Google Vertex AI, using a scoring rubric the employer may replace with its own.
• The productivity percentage and the weekly ranking are arithmetic over those scores: productive time counts in full and neutral time at half, divided by an eight-hour workday. Attendance in the ranking follows the employer's working calendar. The ranking page's written summary of a member's week is produced by the model from these figures.
• The simulated-activity flag combines the model's classification of the segment recording, which it describes, classifies, argues against and classifies again, with fixed checks in our code: score thresholds, subtraction of the device's own idle periods, and a minimum amount of active time before the finding goes to the model's second review.
• Alert matches are the model evaluating the employer's written alert prompts against slices of screen activity. An explanation a manager approves may be used as context in later evaluations for that member.

5. Known failure modes

These outputs are wrong in predictable ways:

• Misread screens. The model can misjudge what is on screen, especially in unfamiliar tools.
• Low-signal periods. Reading, thinking or listening without using the keyboard or mouse becomes away time after the idle threshold (three minutes by default), which lowers the day's percentage.
• Legitimate low-activity work flagged. Work with little but regular pointer movement, such as watching a dashboard while occasionally moving the mouse, can look like simulated activity.
• Language and application variety. Work in less common languages or specialised software is harder to read correctly.
• Transcription errors. Audio transcripts can mishear words or assign them to the wrong speaker.
• The fixed divisor. Because the percentage divides by eight hours, part-time or shorter days score lower whatever their quality.

6. What the employer controls, and what it must do

The employer can shape the outputs through correction rules (standing score overrides for chosen applications or sites), its own scoring rubric, alert prompts, suppression rules that stop alerts during meetings or calls, the exclusion list of applications and sites never captured, and whether simulated-activity detection runs at all and whether flagged time counts as unproductive. Stored screen video (Record + Save) and audio are both off by default, and nobody can redact a time range until the employer grants it.

The employer must treat every score, ranking and flag as an indicator, keep a person in the loop before acting on one, and tell its workers how the service is used before monitoring them.

7. Human review that exists today

Beyond the surfaces listed in section 2:

• a manager approves or rejects a member's explanation in Alerts → Inbox;
• nobody can add a reason over their own time flagged as simulated activity; that is contested through the employer;
• members do not see the Review page, the alerts Log, evidence clips or the ranking.

8. How to contest an output

Ask your employer first. If it does not respond, email support@screenjournal.ai with the subject line "Data protection request"; see our Data Protection Contacts page.

9. Our commitments

• ScreenJournal makes no employment decision about anyone.
• We make no decision based solely on automated processing.
• We describe flags as indicators, never as proof, and tell employers to treat them that way.
• When we change the provider behind these inferences, or how they are produced, we update this statement and list the change on the Legal page.

10. Notes for particular regimes

The notes below are as we understand each regime from published summaries, as at October 2026. They are not legal advice.

• Philippines. NPC Advisory No. 2024-04 expects transparency about an AI system, a privacy impact assessment, meaningful human intervention and a channel to contest outputs. See our Philippines page.
• India. Under the Digital Personal Data Protection Act 2023, an employer's notice purposes should cover scoring, rankings and flags. See our India page.
• United Kingdom. UK employers should consider the UK GDPR's rules on decisions based solely on automated processing (Article 22, and the provisions replacing it as the Data (Use and Access) Act 2025 commences) before relying on any output for a decision with legal or similarly significant effects.
• European Union. The EU AI Act lists AI systems used to monitor and evaluate workers as high-risk (Annex III), and requires employers deploying them to inform workers before use (Article 26). If you deploy ScreenJournal in the EU you must inform workers before use and assess your obligations; this is not legal advice.