Examples:

• • Emma’s staircase fall visualized from multiple heights and angles highlighted risks.
  • AR enabled inspecting David’s crushed car to assess if passenger areas deformed as expected.
  • Jurors stood on digital highway observing visibility issues truckers faced before Jackie’s collision.
  • Crime scene AR overlayed pool area before/after drowning showing gate repairs prevented repeat risks.
  • Nighttime warehouse AR tested lighting contributions to worker slip and falls.

How to Proceed:

• • Consider how affordable access may expand – even smartphones now feature AR capability.
  • Weigh scenarios too expensive, personal or impossible to recreate physically.
  • Ensure technical accuracy – glitches undermine. Secure proof of testing and calibration.
  • Supplement with photography or video capturing nuances algorithms may miss.
  • Remain openminded. No matter how advanced modelling seems skeptics always exist.

FAQs:

• • Could manipulation arguments challenge AR validity? Certainly attempts will arise. Safeguards like encryption and blockchain verification increasingly protect data integrity.
  • What types of cases most easily leverage AR? Product designs, construction zones, vehicle collisions currently have advantage blending digital assets with tangible spaces.
  • Will AR erase need for site visits someday? Unlikely fully. Multi-sensory factors not easily modeled like smells, texture, motion skills. But it narrows required frequency.
  • Can plaintiffs block defense from using AR for strategic advantage? Standard evidentiary rules apply. Both sides typically have opportunities to make arguments.
  • Could AR ever replace physical or even human court appearances? We’ve already seen steps through remote video hearings. Full virtual courts using futuristic tech like holograms seem inevitable longterm.

Examples:

• • Algorithmic analysis appropriately aligned amputee Ray’s future prosthetic needs with compensation.
  • Vast medical record review unearthed pharmacy errors exacerbating already complex spinal damage.
  • AI structured optimal combination of upfront versus deferred payments for Maree based on projected care costs.
  • By inputting education and goals, automated tools appraised scholarship athlete Isla’s statistical income loss.
  • Populationwide insight exposed insurer underpayments correlating to non-native English speakers like Mr. Park

How to Proceed:

• • As tools emerge focusing AI on discovery versus solely dispute resolution.
  • Gather data early before problems compound – outdated records and care gaps raise skepticism.
  • Seek software combining both predictive analytics with emotional intelligence sensing juror reactions.
  • Push developers on transparency – blind faith in equations wanes without understanding origins.
  • Establish human oversight safeguards. Even rigorous programming has limitations needing discretion.

FAQs:

• • Will AI reduce sympathetic awards driven by emotions versus facts? Over time likely. But also promises bringing overlooked nuance like homecare contributions invaluable yet unpaid.
  • What prevents data manipulation attempting to trick AI? Blockchain, stringent review policies, anti-tampering encryption offer security assurances as adoption spreads.
  • Couldn’t programming itself hold hidden biases? Certainly extensive quality control essential across entire supply chain from development, testing to application.
  • Will this widen lawsuit accessibility gaps favoring those already data-rich? Potential danger if not proactively mitigated. Privacy controls paramount especially for vulnerable groups.
  • Is society ready to trust verdicts to algorithms alone? Doubtful soon without human validation. Partnership model makes most sense cooperating AI with lawyers, judges.

Examples:

• • Instagram photos disproved Erica’s disability claims showing her dancing months after accident.
  • Archived tweets revealed bar visits diminishing Waris’s denial around intoxication during his offense.
  • YouTube exposed Amy’s athleticism surpassing described limitations from soft tissue damage.
  • Psychology-detecting tech flagged Claude’s online mannerisms suggesting high insurance fraud risk.
  • Past outrages over “harmless pranks” preceded stunt injuring plaintiff – found on teen’s Reddit history.

How to Proceed:

• • Implement automated reputation scans early establishing baseline character assumptions.
  • Consider reverse searches exposing connections across plaintiffs and defendants.
  • Verify timestamps and geo-locations preventing allegations of fabricated digital evidence.
  • Contrast public posts with non-discoverable restricted account activity.
  • Tread carefully. Ethics dilemmas around privacy and prejudice abound.

FAQs:

• • What prevents someone from hacking profiles planting bogus evidence? Beyond passwords and biometrics, blockchain ledgers theoretically supply manipulation-proof historical verification.
  • Don’t privacy laws limit admissibility and access? They should. But still catching up with digital realities. Common sense judicial discretion key for now.
  • Could psychological targeting online intentionally provoke plaintiffs? Quite possible. But similarly traceable and risky for defense to pursue ethically.
  • What if online posts document emotional distress unrelated to accident? Still potentially relevant assessing total life impacts and claims like pain and suffering.
  • Will juries lose patience for claims contradicted by social media evidence? Likely more skepticism when posts flagrantly oppose central injury assertions over long timespans.